From 07027e8fde9b19c467698fc918fa9ef5db530bab Mon Sep 17 00:00:00 2001
From: syfffffjj <syfffffjj@users.noreply.github.com>
Date: Mon, 2 Nov 2026 16:41:07 +0800
Subject: [PATCH] feat: huoyi M20 smooth/bridge training and stability docs

Current code snapshot: smooth/bridge task configs, smooth random terrain,
roll/pitch stability rewards, M20 URDF updates, eval profile and stability
training documentation.
---
 .gitattributes                                |   18 +
 .gitignore                                    |   39 +
 .vscode/.gitignore                            |   10 +
 .vscode/tools/setup_vscode.py                 |  220 ++++
 CONTRIBUTORS.md                               |   23 +
 LICENSE                                       |   28 +
 LICENSE-robot_lab                             |  201 ++++
 README.md                                     |  180 +++
 README_HUOYI.md                               |   76 ++
 VERSION                                       |    1 +
 deep_robotics_model/LICENSE.txt               |   28 +
 .../meshes_solenopsis_visual/base_link.STL    |  Bin 0 -> 4308584 bytes
 .../meshes_solenopsis_visual/fl_knee.STL      |  Bin 0 -> 720334 bytes
 .../meshes_solenopsis_visual/fl_wheel.STL     |  Bin 0 -> 1139734 bytes
 .../meshes_solenopsis_visual/fr_knee.STL      |  Bin 0 -> 719984 bytes
 .../meshes_solenopsis_visual/fr_wheel.STL     |  Bin 0 -> 1139884 bytes
 .../meshes_solenopsis_visual/hl_knee.STL      |  Bin 0 -> 717384 bytes
 .../meshes_solenopsis_visual/hl_wheel.STL     |  Bin 0 -> 1139584 bytes
 .../meshes_solenopsis_visual/hr_knee.STL      |  Bin 0 -> 716634 bytes
 .../meshes_solenopsis_visual/hr_wheel.STL     |  Bin 0 -> 1139634 bytes
 .../optimization_manifest.json                |   92 ++
 .../M20/M20_urdf/urdf/Solenopsis.urdf         |  292 +++++
 deep_robotics_model/README.md                 |   18 +
 docs/fire_ant_prototype_specs.md              |  201 ++++
 docs/fire_ant_smooth_training.md              |  561 +++++++++
 docs/imgs/deeprobotics_lite3.png              |  Bin 0 -> 150580 bytes
 docs/imgs/deeprobotics_m20.png                |  Bin 0 -> 208383 bytes
 docs/motor_model_update_comparison.md         |   51 +
 docs/solenopsis_urdf_comparison.md            |  114 ++
 eval_kit/README.md                            |  561 +++++++++
 eval_kit/__init__.py                          |   35 +
 eval_kit/core/__init__.py                     |   26 +
 eval_kit/core/aggregation.py                  |  212 ++++
 eval_kit/core/metrics.py                      |  262 +++++
 eval_kit/core/naming.py                       |   94 ++
 eval_kit/core/report.py                       |  191 +++
 eval_kit/core/schema.py                       |  392 +++++++
 eval_kit/core/terrain_spec.py                 |  157 +++
 eval_kit/isaaclab/__init__.py                 |   18 +
 eval_kit/isaaclab/env_setup.py                |  307 +++++
 eval_kit/isaaclab/policy.py                   |   71 ++
 eval_kit/isaaclab/recorder.py                 |  257 +++++
 eval_kit/profiles.py                          |   56 +
 eval_kit/protocols.py                         |  102 ++
 eval_kit/tests/test_cli.py                    |  544 +++++++++
 eval_kit/tests/test_core.py                   |  645 +++++++++++
 pyproject.toml                                |   85 ++
 scripts/reinforcement_learning/rl_utils.py    |   44 +
 .../reinforcement_learning/rsl_rl/cli_args.py |   97 ++
 scripts/reinforcement_learning/rsl_rl/eval.py |  716 ++++++++++++
 scripts/reinforcement_learning/rsl_rl/eval.sh |   23 +
 .../rsl_rl/eval_suite.py                      |  893 ++++++++++++++
 scripts/reinforcement_learning/rsl_rl/play.py |  285 +++++
 .../reinforcement_learning/rsl_rl/train.py    |  214 ++++
 .../tools/generate_solenopsis_cad_meshes.py   |  306 +++++
 scripts/tools/list_envs.py                    |   80 ++
 scripts/tools/optimize_solenopsis_stl.py      |  100 ++
 source/rl_training/config/extension.toml      |   35 +
 source/rl_training/pyproject.toml             |    3 +
 source/rl_training/rl_training/__init__.py    |   15 +
 .../rl_training/assets/__init__.py            |   32 +
 .../rl_training/assets/deeprobotics.py        |  125 ++
 .../rl_training/rl_training/tasks/__init__.py |   27 +
 .../tasks/manager_based/__init__.py           |   16 +
 .../manager_based/locomotion/__init__.py      |   14 +
 .../locomotion/velocity/__init__.py           |   18 +
 .../locomotion/velocity/config/__init__.py    |   15 +
 .../velocity/config/quadruped/__init__.py     |   15 +
 .../quadruped/deeprobotics_lite3/__init__.py  |   36 +
 .../deeprobotics_lite3/agents/__init__.py     |    5 +
 .../agents/rsl_rl_ppo_cfg.py                  |   48 +
 .../deeprobotics_lite3/flat_env_cfg.py        |   32 +
 .../deeprobotics_lite3/rough_env_cfg.py       |  183 +++
 .../velocity/config/wheeled/__init__.py       |   15 +
 .../wheeled/deeprobotics_m20/__init__.py      |   68 ++
 .../deeprobotics_m20/agents/__init__.py       |    5 +
 .../deeprobotics_m20/agents/rsl_rl_ppo_cfg.py |   78 ++
 .../deeprobotics_m20/bridge_env_cfg.py        |   76 ++
 .../wheeled/deeprobotics_m20/flat_env_cfg.py  |   32 +
 .../wheeled/deeprobotics_m20/rough_env_cfg.py |  475 ++++++++
 .../deeprobotics_m20/smooth_env_cfg.py        |   79 ++
 .../locomotion/velocity/mdp/__init__.py       |   22 +
 .../locomotion/velocity/mdp/commands.py       |  130 +++
 .../locomotion/velocity/mdp/curriculums.py    |   95 ++
 .../locomotion/velocity/mdp/events.py         |  213 ++++
 .../locomotion/velocity/mdp/observations.py   |  105 ++
 .../locomotion/velocity/mdp/rewards.py        | 1027 +++++++++++++++++
 .../locomotion/velocity/mdp/terrain.py        |  107 ++
 .../locomotion/velocity/velocity_env_cfg.py   |  868 ++++++++++++++
 .../rl_training/ui_extension_example.py       |   52 +
 source/rl_training/setup.py                   |   55 +
 91 files changed, 13047 insertions(+)
 create mode 100644 .gitattributes
 create mode 100644 .gitignore
 create mode 100644 .vscode/.gitignore
 create mode 100644 .vscode/tools/setup_vscode.py
 create mode 100644 CONTRIBUTORS.md
 create mode 100644 LICENSE
 create mode 100644 LICENSE-robot_lab
 create mode 100644 README.md
 create mode 100644 README_HUOYI.md
 create mode 100644 VERSION
 create mode 100644 deep_robotics_model/LICENSE.txt
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/base_link.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fl_knee.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fl_wheel.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fr_knee.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fr_wheel.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hl_knee.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hl_wheel.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hr_knee.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hr_wheel.STL
 create mode 100644 deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/optimization_manifest.json
 create mode 100644 deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf
 create mode 100644 deep_robotics_model/README.md
 create mode 100644 docs/fire_ant_prototype_specs.md
 create mode 100644 docs/fire_ant_smooth_training.md
 create mode 100644 docs/imgs/deeprobotics_lite3.png
 create mode 100644 docs/imgs/deeprobotics_m20.png
 create mode 100644 docs/motor_model_update_comparison.md
 create mode 100644 docs/solenopsis_urdf_comparison.md
 create mode 100644 eval_kit/README.md
 create mode 100644 eval_kit/__init__.py
 create mode 100644 eval_kit/core/__init__.py
 create mode 100644 eval_kit/core/aggregation.py
 create mode 100644 eval_kit/core/metrics.py
 create mode 100644 eval_kit/core/naming.py
 create mode 100644 eval_kit/core/report.py
 create mode 100644 eval_kit/core/schema.py
 create mode 100644 eval_kit/core/terrain_spec.py
 create mode 100644 eval_kit/isaaclab/__init__.py
 create mode 100644 eval_kit/isaaclab/env_setup.py
 create mode 100644 eval_kit/isaaclab/policy.py
 create mode 100644 eval_kit/isaaclab/recorder.py
 create mode 100644 eval_kit/profiles.py
 create mode 100644 eval_kit/protocols.py
 create mode 100644 eval_kit/tests/test_cli.py
 create mode 100644 eval_kit/tests/test_core.py
 create mode 100644 pyproject.toml
 create mode 100644 scripts/reinforcement_learning/rl_utils.py
 create mode 100644 scripts/reinforcement_learning/rsl_rl/cli_args.py
 create mode 100644 scripts/reinforcement_learning/rsl_rl/eval.py
 create mode 100755 scripts/reinforcement_learning/rsl_rl/eval.sh
 create mode 100644 scripts/reinforcement_learning/rsl_rl/eval_suite.py
 create mode 100644 scripts/reinforcement_learning/rsl_rl/play.py
 create mode 100644 scripts/reinforcement_learning/rsl_rl/train.py
 create mode 100644 scripts/tools/generate_solenopsis_cad_meshes.py
 create mode 100644 scripts/tools/list_envs.py
 create mode 100644 scripts/tools/optimize_solenopsis_stl.py
 create mode 100644 source/rl_training/config/extension.toml
 create mode 100644 source/rl_training/pyproject.toml
 create mode 100644 source/rl_training/rl_training/__init__.py
 create mode 100644 source/rl_training/rl_training/assets/__init__.py
 create mode 100644 source/rl_training/rl_training/assets/deeprobotics.py
 create mode 100644 source/rl_training/rl_training/tasks/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/rsl_rl_ppo_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/flat_env_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/rough_env_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/rsl_rl_ppo_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/bridge_env_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/flat_env_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/smooth_env_cfg.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/__init__.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/commands.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/curriculums.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/events.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/observations.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/rewards.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/terrain.py
 create mode 100644 source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/velocity_env_cfg.py
 create mode 100644 source/rl_training/rl_training/ui_extension_example.py
 create mode 100644 source/rl_training/setup.py

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..d917487
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,18 @@
+# copied and modified from https://github.com/NVIDIA/warp/blob/main/.gitattributes
+* text=auto
+*.sh text eol=LF
+
+# # copied from https://github.com/isaac-sim/IsaacLab/blob/main/.gitattributes
+# *.usd filter=lfs diff=lfs merge=lfs -text
+# *.dae filter=lfs diff=lfs merge=lfs -text
+# *.mtl filter=lfs diff=lfs merge=lfs -text
+# *.obj filter=lfs diff=lfs merge=lfs -text
+# *.gif filter=lfs diff=lfs merge=lfs -text
+# *.png filter=lfs diff=lfs merge=lfs -text
+# *.jpg filter=lfs diff=lfs merge=lfs -text
+# *.psd filter=lfs diff=lfs merge=lfs -text
+# *.mp4 filter=lfs diff=lfs merge=lfs -text
+# *.usda filter=lfs diff=lfs merge=lfs -text
+# *.hdr filter=lfs diff=lfs merge=lfs -text
+# *.pt filter=lfs diff=lfs merge=lfs -text
+# *.jit filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..389dd89
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,39 @@
+# Omniverse
+**/*.dmp
+**/.thumbs
+
+# Python
+.DS_Store
+**/*.egg-info/
+**/__pycache__/
+**/.pytest_cache/
+**/*.pyc
+**/*.pb
+
+# IDE
+**/.idea/
+**/.vscode/
+# Don't ignore the top-level .vscode directory as it is
+# used to configure VS Code settings
+!.vscode
+
+# Outputs
+**/runs/*
+**/logs/*
+**/recordings/*
+**/output/*
+**/outputs/*
+**/videos/*
+**/wandb/*
+**/.neptune/*
+docker/artifacts/
+*.tmp
+
+# Isaac-Sim packman
+_isaac_sim*
+_repo
+_build
+.lastformat
+
+
+**/usd/*
diff --git a/.vscode/.gitignore b/.vscode/.gitignore
new file mode 100644
index 0000000..10b0af3
--- /dev/null
+++ b/.vscode/.gitignore
@@ -0,0 +1,10 @@
+# Note: These files are kept for development purposes only.
+!tools/launch.template.json
+!tools/settings.template.json
+!tools/setup_vscode.py
+!extensions.json
+!tasks.json
+
+# Ignore all other files
+.python.env
+*.json
diff --git a/.vscode/tools/setup_vscode.py b/.vscode/tools/setup_vscode.py
new file mode 100644
index 0000000..07bf6f4
--- /dev/null
+++ b/.vscode/tools/setup_vscode.py
@@ -0,0 +1,220 @@
+# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""This script sets up the vs-code settings for the Isaac Lab project.
+
+This script merges the python.analysis.extraPaths from the "{ISAACSIM_DIR}/.vscode/settings.json" file into
+the ".vscode/settings.json" file.
+
+This is necessary because Isaac Sim 2022.2.1 onwards does not add the necessary python packages to the python path
+when the "setup_python_env.sh" is run as part of the vs-code launch configuration.
+"""
+
+import argparse
+import os
+import pathlib
+import platform
+import re
+import sys
+
+PROJECT_DIR = pathlib.Path(__file__).parents[2]
+"""Path to the the project directory."""
+
+try:
+    import isaacsim  # noqa: F401
+
+    isaacsim_dir = os.environ.get("ISAAC_PATH", "")
+except ModuleNotFoundError or ImportError:
+    # Create a parser to get the isaac-sim path
+    parser = argparse.ArgumentParser(description="Setup the VSCode settings for the project.")
+    parser.add_argument("--isaac_path", type=str, help="The absolute path to the Isaac Sim installation.")
+    args = parser.parse_args()
+
+    # parse the isaac-sim directory
+    isaacsim_dir = args.isaac_path
+    # check if the isaac-sim directory is provided
+    if not os.path.exists(isaacsim_dir):
+        raise FileNotFoundError(
+            f"Could not find the isaac-sim directory: {isaacsim_dir}. Please provide the correct path to the Isaac Sim"
+            " installation."
+        )
+except EOFError:
+    print("Unable to trigger EULA acceptance. This is likely due to the script being run in a non-interactive shell.")
+    print("Please run the script in an interactive shell to accept the EULA.")
+    print("Skipping the setup of the VSCode settings...")
+    sys.exit(0)
+
+# check if the isaac-sim directory exists
+if not os.path.exists(isaacsim_dir):
+    raise FileNotFoundError(
+        f"Could not find the isaac-sim directory: {isaacsim_dir}. There are two possible reasons for this:"
+        "\n\t1. The Isaac Sim directory does not exist as provided CLI path."
+        "\n\t2. The script could import the 'isaacsim' package. This could be due to the 'isaacsim' package not being "
+        "installed in the Python environment.\n"
+        "\nPlease make sure that the Isaac Sim directory exists or that the 'isaacsim' package is installed."
+    )
+
+ISAACSIM_DIR = isaacsim_dir
+"""Path to the isaac-sim directory."""
+
+
+def overwrite_python_analysis_extra_paths(isaaclab_settings: str) -> str:
+    """Overwrite the python.analysis.extraPaths in the Isaac Lab settings file.
+
+    The extraPaths are replaced with the path names from the isaac-sim settings file that exists in the
+    "{ISAACSIM_DIR}/.vscode/settings.json" file.
+
+    If the isaac-sim settings file does not exist, the extraPaths are not overwritten.
+
+    Args:
+        isaaclab_settings: The settings string to use as template.
+
+    Returns:
+        The settings string with overwritten python analysis extra paths.
+    """
+    # isaac-sim settings
+    isaacsim_vscode_filename = os.path.join(ISAACSIM_DIR, ".vscode", "settings.json")
+
+    # we use the isaac-sim settings file to get the python.analysis.extraPaths for kit extensions
+    # if this file does not exist, we will not add any extra paths
+    if os.path.exists(isaacsim_vscode_filename):
+        # read the path names from the isaac-sim settings file
+        with open(isaacsim_vscode_filename) as f:
+            vscode_settings = f.read()
+        # extract the path names
+        # search for the python.analysis.extraPaths section and extract the contents
+        settings = re.search(
+            r"\"python.analysis.extraPaths\": \[.*?\]", vscode_settings, flags=re.MULTILINE | re.DOTALL
+        )
+        settings = settings.group(0)
+        settings = settings.split('"python.analysis.extraPaths": [')[-1]
+        settings = settings.split("]")[0]
+
+        # read the path names from the isaac-sim settings file
+        path_names = settings.split(",")
+        path_names = [path_name.strip().strip('"') for path_name in path_names]
+        path_names = [path_name for path_name in path_names if len(path_name) > 0]
+
+        # change the path names to be relative to the Isaac Lab directory
+        rel_path = os.path.relpath(ISAACSIM_DIR, PROJECT_DIR)
+        path_names = ['"${workspaceFolder}/' + rel_path + "/" + path_name + '"' for path_name in path_names]
+    else:
+        path_names = []
+        print(
+            f"[WARN] Could not find Isaac Sim VSCode settings: {isaacsim_vscode_filename}."
+            "\n\tThis will result in missing 'python.analysis.extraPaths' in the VSCode"
+            "\n\tsettings, which limits the functionality of the Python language server."
+            "\n\tHowever, it does not affect the functionality of the Isaac Lab project."
+            "\n\tWe are working on a fix for this issue with the Isaac Sim team."
+        )
+
+    # add the path names that are in the Isaac Lab extensions directory
+    isaaclab_extensions = os.listdir(os.path.join(PROJECT_DIR, "source"))
+    path_names.extend(['"${workspaceFolder}/source/' + ext + '"' for ext in isaaclab_extensions])
+
+    # combine them into a single string
+    path_names = ",\n\t\t".expandtabs(4).join(path_names)
+    # deal with the path separator being different on Windows and Unix
+    path_names = path_names.replace("\\", "/")
+
+    # replace the path names in the Isaac Lab settings file with the path names parsed
+    isaaclab_settings = re.sub(
+        r"\"python.analysis.extraPaths\": \[.*?\]",
+        '"python.analysis.extraPaths": [\n\t\t'.expandtabs(4) + path_names + "\n\t]".expandtabs(4),
+        isaaclab_settings,
+        flags=re.DOTALL,
+    )
+    # return the Isaac Lab settings string
+    return isaaclab_settings
+
+
+def overwrite_default_python_interpreter(isaaclab_settings: str) -> str:
+    """Overwrite the default python interpreter in the Isaac Lab settings file.
+
+    The default python interpreter is replaced with the path to the python interpreter used by the
+    isaac-sim project. This is necessary because the default python interpreter is the one shipped with
+    isaac-sim.
+
+    Args:
+        isaaclab_settings: The settings string to use as template.
+
+    Returns:
+        The settings string with overwritten default python interpreter.
+    """
+    # read executable name
+    python_exe = os.path.normpath(sys.executable)
+
+    # replace with Isaac Sim's python.sh or python.bat scripts to make sure python with correct
+    # source paths is set as default
+    if f"kit{os.sep}python{os.sep}bin{os.sep}python" in python_exe:
+        # Check if the OS is Windows or Linux to use appropriate shell file
+        if platform.system() == "Windows":
+            python_exe = python_exe.replace(f"kit{os.sep}python{os.sep}bin{os.sep}python3", "python.bat")
+        else:
+            python_exe = python_exe.replace(f"kit{os.sep}python{os.sep}bin{os.sep}python3", "python.sh")
+
+    # replace the default python interpreter in the Isaac Lab settings file with the path to the
+    # python interpreter in the Isaac Lab directory
+    isaaclab_settings = re.sub(
+        r"\"python.defaultInterpreterPath\": \".*?\"",
+        f'"python.defaultInterpreterPath": "{python_exe}"',
+        isaaclab_settings,
+        flags=re.DOTALL,
+    )
+    # return the Isaac Lab settings file
+    return isaaclab_settings
+
+
+def main():
+    # Isaac Lab template settings
+    isaaclab_vscode_template_filename = os.path.join(PROJECT_DIR, ".vscode", "tools", "settings.template.json")
+    # make sure the Isaac Lab template settings file exists
+    if not os.path.exists(isaaclab_vscode_template_filename):
+        raise FileNotFoundError(
+            f"Could not find the Isaac Lab template settings file: {isaaclab_vscode_template_filename}"
+        )
+    # read the Isaac Lab template settings file
+    with open(isaaclab_vscode_template_filename) as f:
+        isaaclab_template_settings = f.read()
+
+    # overwrite the python.analysis.extraPaths in the Isaac Lab settings file with the path names
+    isaaclab_settings = overwrite_python_analysis_extra_paths(isaaclab_template_settings)
+    # overwrite the default python interpreter in the Isaac Lab settings file with the path to the
+    # python interpreter used to call this script
+    isaaclab_settings = overwrite_default_python_interpreter(isaaclab_settings)
+
+    # add template notice to the top of the file
+    header_message = (
+        "// This file is a template and is automatically generated by the setup_vscode.py script.\n"
+        "// Do not edit this file directly.\n"
+        "// \n"
+        f"// Generated from: {isaaclab_vscode_template_filename}\n"
+    )
+    isaaclab_settings = header_message + isaaclab_settings
+
+    # write the Isaac Lab settings file
+    isaaclab_vscode_filename = os.path.join(PROJECT_DIR, ".vscode", "settings.json")
+    with open(isaaclab_vscode_filename, "w") as f:
+        f.write(isaaclab_settings)
+
+    # copy the launch.json file if it doesn't exist
+    isaaclab_vscode_launch_filename = os.path.join(PROJECT_DIR, ".vscode", "launch.json")
+    isaaclab_vscode_template_launch_filename = os.path.join(PROJECT_DIR, ".vscode", "tools", "launch.template.json")
+    if not os.path.exists(isaaclab_vscode_launch_filename):
+        # read template launch settings
+        with open(isaaclab_vscode_template_launch_filename) as f:
+            isaaclab_template_launch_settings = f.read()
+        # add header
+        header_message = header_message.replace(
+            isaaclab_vscode_template_filename, isaaclab_vscode_template_launch_filename
+        )
+        isaaclab_launch_settings = header_message + isaaclab_template_launch_settings
+        # write the Isaac Lab launch settings file
+        with open(isaaclab_vscode_launch_filename, "w") as f:
+            f.write(isaaclab_launch_settings)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
new file mode 100644
index 0000000..829eafb
--- /dev/null
+++ b/CONTRIBUTORS.md
@@ -0,0 +1,23 @@
+# RL Training Developers and Contributors
+
+This is the official list of RL Training Project developers and contributors.
+
+To see the full list of contributors, please check the revision history in the source control.
+
+Guidelines for modifications:
+
+* Please keep the lists sorted alphabetically.
+* Names should be added to this file as: *individual names* or *organizations*.
+
+## Developers
+
+* [Bo Peng](https://www.linkedin.com/in/bo-peng-647068272/)
+
+## Contributors
+
+* [Ziqi Fan](https://github.com/fan-ziqi)
+* Zhongliang Pan
+
+## Acknowledgements
+
+
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..6c1a48b
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,28 @@
+BSD 3-Clause License
+
+Copyright (c) 2025, DEEPRoboticsLab
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/LICENSE-robot_lab b/LICENSE-robot_lab
new file mode 100644
index 0000000..e08a51a
--- /dev/null
+++ b/LICENSE-robot_lab
@@ -0,0 +1,201 @@
+                                 Apache License
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+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+   1. Definitions.
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+      the conditions stated in this License.
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
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+   9. Accepting Warranty or Additional Liability. While redistributing
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+      on Your own behalf and on Your sole responsibility, not on behalf
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+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
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+   Copyright 2024-2025 Ziqi Fan
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
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+   See the License for the specific language governing permissions and
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..4c5228e
--- /dev/null
+++ b/README.md
@@ -0,0 +1,180 @@
+rl_training — 火蚁独立训练版本
+
+> [!IMPORTANT]
+> 本仓库是火蚁（Solenopsis）轮足平台的独立发布副本。火蚁专用训练、Stage 2、URDF与资产说明请先阅读 [README_HUOYI.md](README_HUOYI.md)。模型资产已直接包含在仓库中，不需要初始化 Git 子模块。
+
+[![IsaacSim](https://img.shields.io/badge/IsaacSim-5.1.0-silver.svg)](https://docs.omniverse.nvidia.com/isaacsim/latest/overview.html)
+[![Isaac Lab](https://img.shields.io/badge/IsaacLab-2.3.0-silver)](https://isaac-sim.github.io/IsaacLab)
+[![Discord](https://img.shields.io/badge/-Discord-5865F2?style=flat&logo=Discord&logoColor=white)](https://discord.gg/gdM9mQutC8)
+[![Python](https://img.shields.io/badge/python-3.11-blue.svg)](https://docs.python.org/3/whatsnew/3.11.html)
+[![Linux platform](https://img.shields.io/badge/platform-linux--64-orange.svg)](https://releases.ubuntu.com/22.04/)
+[![License](https://img.shields.io/badge/license-BSD%203--Clause-blue.svg)](https://opensource.org/license/bsd-3-clause)
+
+## Tutorial Videos
+We've released the following tutorials for training and deploying a reinforcement learning policy. Please check it out on [Bilibili](https://b23.tv/UoIqsFn) or [YouTube](https://youtube.com/playlist?list=PLy9YHJvMnjO0X4tx_NTWugTUMJXUrOgFH&si=pjUGF5PbFf3tGLFz)! 
+
+## Overview
+
+**rl_training** is a RL training library for deeprobotics robots, based on IsaacLab. The table below lists all available environments:
+
+| Robot Model         | Environment Name (ID)                                      | Screenshot |
+|---------------------|------------------------------------------------------------|------------|
+| [Deeprobotics Lite3](https://www.deeprobotics.cn/robot/index/product1.html) | Rough-Deeprobotics-Lite3-v0 | <img src="./docs/imgs/deeprobotics_lite3.png" alt="Lite3" width="300">
+| [Deeprobotics M20](https://www.deeprobotics.cn/robot/index/lynx.html) | Rough-Deeprobotics-M20-v0 | <img src="./docs/imgs/deeprobotics_m20.png" alt="deeprobotics_m20" width="300">
+
+> [!NOTE]
+> If you want to deploy policies in mujoco or real robots, please use the corresponding deploy repo in [Deep Robotics Github Center](https://github.com/DeepRoboticsLab).
+## Contribution 
+
+Everyone is welcome to contribute to this repo. If you discover a bug or optimize our training config, just submit a pull request and we will look into it.
+## Installation
+
+- Install Isaac Lab by following the [installation guide](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html). We recommend using the conda installation as it simplifies calling Python scripts from the terminal.
+
+- Clone this repository separately from the Isaac Lab installation (i.e. outside the `IsaacLab` directory):
+
+  ```bash
+  git clone https://github.com/weifenghuang812/huoyi_training.git
+  cd huoyi_training
+  ```
+
+- Using a python interpreter that has Isaac Lab installed, install the library
+
+  ```bash
+  python -m pip install -e source/rl_training
+  ```
+
+- Verify that the extension is correctly installed by running the following command to print all the available environments in the extension:
+
+  ```bash
+  python scripts/tools/list_envs.py
+  ```
+
+<details>
+
+<summary>Setup as Omniverse Extension (Optional, click to expand)</summary>
+
+We provide an example UI extension that will load upon enabling your extension defined in `source/rl_training/rl_training/ui_extension_example.py`.
+
+To enable your extension, follow these steps:
+
+1. **Add the search path of your repository** to the extension manager:
+    - Navigate to the extension manager using `Window` -> `Extensions`.
+    - Click on the **Hamburger Icon** (☰), then go to `Settings`.
+    - In the `Extension Search Paths`, enter the absolute path to `rl_trainingb/source`
+    - If not already present, in the `Extension Search Paths`, enter the path that leads to Isaac Lab's extension directory directory (`IsaacLab/source`)
+    - Click on the **Hamburger Icon** (☰), then click `Refresh`.
+
+2. **Search and enable your extension**:
+    - Find your extension under the `Third Party` category.
+    - Toggle it to enable your extension.
+
+</details>
+
+## Try examples
+
+Deeprobotics Lite3:
+
+```bash
+# Train
+python scripts/reinforcement_learning/rsl_rl/train.py --task=Rough-Deeprobotics-Lite3-v0 --headless
+
+# Play
+python scripts/reinforcement_learning/rsl_rl/play.py --task=Rough-Deeprobotics-Lite3-v0 --num_envs=10
+```
+
+Deeprobotics M20:
+
+```bash
+# Train
+python scripts/reinforcement_learning/rsl_rl/train.py --task=Rough-Deeprobotics-M20-v0 --headless
+
+# Play
+python scripts/reinforcement_learning/rsl_rl/play.py --task=Rough-Deeprobotics-M20-v0 --num_envs=10
+```
+
+> [!NOTE]
+> If you want to control a **SINGLE ROBOT** with the keyboard during playback, add `--keyboard` at the end of the play script.
+>
+> ```
+> Key bindings:
+> ====================== ========================= ========================
+> Command                Key (+ve axis)            Key (-ve axis)
+> ====================== ========================= ========================
+> Move along x-axis      Numpad 8 / Arrow Up       Numpad 2 / Arrow Down
+> Move along y-axis      Numpad 4 / Arrow Right    Numpad 6 / Arrow Left
+> Rotate along z-axis    Numpad 7 / Z              Numpad 9 / X
+> ====================== ========================= ========================
+> ```
+* Record video of a trained agent (requires installing `ffmpeg`), add `--video --video_length 200`
+* Play/Train with 32 environments, add `--num_envs 32`
+* Play on specific folder or checkpoint, add `--load_run run_folder_name --checkpoint model.pt`
+* Resume training from folder or checkpoint, add `--resume --load_run run_folder_name --checkpoint model.pt`
+
+## Trained Results
+You can download our trained examples for reference of the training process. This should be expected if you run our training process properly. [M20](https://drive.google.com/file/d/1PAGIHOI6Lsge_JA9kU3w7RU-GyqXybob/view?usp=drive_link) and [Lite3](https://drive.google.com/file/d/1EgCVQA552x3YiaRhPRv675TSd01yqF0c/view?usp=drive_link)
+
+
+
+## Multi-gpu acceleration
+* To train with multiple GPUs, use the following command, where --nproc_per_node represents the number of available GPUs:
+    ```bash
+    python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless 
+    python -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/reinforcement_learning/rsl_rl/train.py --task=Rough-Deeprobotics-Lite3-v0 --headless --distributed --num_envs=2048
+    ```
+* Note: each gpu will have the same number of envs specified in the config, to use the previous total number of envs, devide it by the number of gpus.
+* To scale up training beyond multiple GPUs on a single machine, it is also possible to train across multiple nodes. To train across multiple nodes/machines, it is required to launch an individual process on each node.
+
+    For the master node, use the following command, where --nproc_per_node represents the number of available GPUs, and --nnodes represents the number of nodes:
+    ```bash
+    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed
+    ```
+    Note that the port (`5555`) can be replaced with any other available port.
+    For non-master nodes, use the following command, replacing `--node_rank` with the index of each machine:
+    ```bash
+    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=<ENV_NAME> --headless --distributed
+    ```
+
+## Tensorboard
+
+To view tensorboard, run:
+
+```bash
+tensorboard --logdir=logs
+```
+
+## Troubleshooting
+
+### Pylance Missing Indexing of Extensions
+
+In some VsCode versions, the indexing of part of the extensions is missing. In this case, add the path to your extension in `.vscode/settings.json` under the key `"python.analysis.extraPaths"`.
+
+**Note: Replace `<path-to-isaac-lab>` with your own IsaacLab path.**
+
+```json
+{
+    "python.languageServer": "Pylance",
+    "python.analysis.extraPaths": [
+        "${workspaceFolder}/source/rl_training",
+        "/<path-to-isaac-lab>/source/isaaclab",
+        "/<path-to-isaac-lab>/source/isaaclab_assets",
+        "/<path-to-isaac-lab>/source/isaaclab_mimic",
+        "/<path-to-isaac-lab>/source/isaaclab_rl",
+        "/<path-to-isaac-lab>/source/isaaclab_tasks",
+    ]
+}
+```
+
+### Clean USD Caches
+
+Temporary USD files are generated in `/tmp/IsaacLab/usd_{date}_{time}_{random}` during simulation runs. These files can consume significant disk space and can be cleaned by:
+
+```bash
+rm -rf /tmp/IsaacLab/usd_*
+```
+
+## Acknowledgements
+
+The project uses some code from the following open-source code repositories:
+
+- [fan-ziqi/robot_lab](https://github.com/fan-ziqi/robot_lab)
diff --git a/README_HUOYI.md b/README_HUOYI.md
new file mode 100644
index 0000000..6985a9b
--- /dev/null
+++ b/README_HUOYI.md
@@ -0,0 +1,76 @@
+# 火蚁轮足机器人强化学习项目
+
+这是火蚁（Solenopsis）轮足平台当前可运行训练版本的独立发布副本。
+
+## 包含内容
+
+- Isaac Lab / RSL-RL 训练与播放脚本
+- 火蚁 Stage 1 与 Stage 2 多地形任务配置
+- 火蚁质量、惯量、电机和接触参数
+- `Solenopsis.urdf`
+- 当前 URDF 使用的轻量可视化 STL（碰撞体仍为简化几何体）
+- 评估脚本和模型对比文档
+
+为了保持仓库轻量，本仓库不包含训练日志、TensorBoard 数据、历史 checkpoint、原始高精度 CAD STL、旧 USD 备份或本地 Codex 快照。
+
+## 克隆
+
+```bash
+git clone https://github.com/weifenghuang812/huoyi_training.git
+cd huoyi_training
+```
+
+本仓库已经将火蚁模型直接包含在 `deep_robotics_model/` 中，不需要执行 `git submodule update`。
+
+## 安装
+
+先按项目原始 `README.md` 安装匹配版本的 Isaac Lab，然后在仓库根目录执行：
+
+```bash
+python -m pip install -e source/rl_training
+```
+
+## 从零训练 Stage 1
+
+```bash
+python scripts/reinforcement_learning/rsl_rl/train.py \
+  --task Rough-Deeprobotics-M20-v0 \
+  --headless
+```
+
+## 训练 Stage 2
+
+Stage 2 应从 Stage 1 checkpoint 恢复：
+
+```bash
+python scripts/reinforcement_learning/rsl_rl/train.py \
+  --task Rough-Deeprobotics-M20-Support-v0 \
+  --headless \
+  --resume \
+  --load_run /绝对路径/到/stage1运行目录 \
+  --load_checkpoint model_xxx.pt
+```
+
+## 播放
+
+```bash
+python scripts/reinforcement_learning/rsl_rl/play.py \
+  --task Rough-Deeprobotics-M20-v0 \
+  --checkpoint /绝对路径/到/model_xxx.pt
+```
+
+键盘控制时添加：
+
+```bash
+--keyboard
+```
+
+## 资产说明
+
+活动模型路径为：
+
+```text
+deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf
+```
+
+训练加载 URDF；碰撞体使用方块和圆柱简化，可视化使用轻量 STL。
diff --git a/VERSION b/VERSION
new file mode 100644
index 0000000..afaf360
--- /dev/null
+++ b/VERSION
@@ -0,0 +1 @@
+1.0.0
\ No newline at end of file
diff --git a/deep_robotics_model/LICENSE.txt b/deep_robotics_model/LICENSE.txt
new file mode 100644
index 0000000..cdc6c06
--- /dev/null
+++ b/deep_robotics_model/LICENSE.txt
@@ -0,0 +1,28 @@
+BSD 3-Clause License
+
+Copyright (c) 2024, DeepRoboticsLab
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/base_link.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/base_link.STL
new file mode 100644
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fl_knee.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fl_knee.STL
new file mode 100644
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fl_wheel.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fl_wheel.STL
new file mode 100644
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fr_knee.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fr_knee.STL
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fr_wheel.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/fr_wheel.STL
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hl_knee.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hl_knee.STL
new file mode 100644
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hl_wheel.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hl_wheel.STL
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hr_knee.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hr_knee.STL
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hr_wheel.STL b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/hr_wheel.STL
new file mode 100644
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diff --git a/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/optimization_manifest.json b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/optimization_manifest.json
new file mode 100644
index 0000000..7ffc024
--- /dev/null
+++ b/deep_robotics_model/M20/M20_urdf/meshes_solenopsis_visual/optimization_manifest.json
@@ -0,0 +1,92 @@
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+      "file": "hr_knee.STL",
+      "source_vertices": 54172,
+      "source_faces": 108311,
+      "optimized_vertices": 6331,
+      "optimized_faces": 14331,
+      "source_bytes": 5415634,
+      "optimized_bytes": 716634
+    },
+    {
+      "file": "hr_wheel.STL",
+      "source_vertices": 71653,
+      "source_faces": 143194,
+      "optimized_vertices": 11121,
+      "optimized_faces": 22791,
+      "source_bytes": 7159784,
+      "optimized_bytes": 1139634
+    }
+  ]
+}
diff --git a/deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf b/deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf
new file mode 100644
index 0000000..1240b9b
--- /dev/null
+++ b/deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf
@@ -0,0 +1,292 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+  Solenopsis fire-ant, generated from the 2026 SolidWorks STEP assembly.
+
+  Units: metre, kilogram, radian, kg*m^2.
+  Coordinate conversion: (x,y,z)_URDF = (z,x,y)_SolidWorks.
+  All inertia tensors are about each rigid body's own centre of mass.
+
+  Visual geometry uses the STEP-derived STL files in
+  ../meshes_solenopsis_visual at a millimetre-to-metre scale of 0.001.
+  Collision geometry remains primitive boxes and cylinders.
+
+  The 0.127 kg gap between the supplied rigid-group masses and the supplied
+  whole-robot mass is lumped into base_link at the reported base COM.  Thus
+  base mass is 17.274 + 0.127 = 17.401 kg, while its reported COM and inertia
+  tensor are unchanged.  No spatial distribution is invented for that mass.
+-->
+<robot name="Solenopsis_fire_ant">
+
+  <link name="base_link">
+    <inertial>
+      <origin xyz="-0.000072 0.000171 0.076892" rpy="0 0 0"/>
+      <mass value="15.84"/>
+      <inertia ixx="0.121947189" ixy="-0.000666758" ixz="0.000143011"
+               iyy="0.331070194" iyz="0.000150561" izz="0.410284855"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/base_link.STL" scale="0.001 0.001 0.001"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 0 0.0788" rpy="0 0 0"/>
+      <geometry>
+        <box size="0.706 0.310 0.1714"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <!-- Front-left.  Left-link tensors use the exact proper rotation
+       (x,y,z)_link = (-z,-x,y)_SolidWorks-module. -->
+  <link name="fl_knee">
+    <inertial>
+      <origin xyz="-0.000040 -0.001088 -0.158988" rpy="0 0 0"/>
+      <mass value="0.885"/>
+      <inertia ixx="0.013089177" ixy="0.000002705" ixz="0.000004495"
+               iyy="0.013409459" iyz="-0.000242701" izz="0.000443796"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/fl_knee.STL" scale="0.001 0.001 0.001"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 -0.0005 -0.15575" rpy="0 0 0"/>
+      <geometry>
+        <box size="0.088 0.031 0.3765"/>
+      </geometry>
+    </collision>
+    <collision>
+      <origin xyz="0 -0.02625 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.0205" radius="0.01995"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="fl_knee_joint" type="revolute">
+    <origin xyz="0.117577164466 0.167 0.070" rpy="0 0 0"/>
+    <parent link="base_link"/>
+    <child link="fl_knee"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="-2.792" upper="2.809" effort="75.0" velocity="17.8024"/>
+  </joint>
+
+  <link name="fl_wheel">
+    <inertial>
+      <origin xyz="-0.000002 0.064 -0.000009" rpy="0 0 0"/>
+      <mass value="1.340"/>
+      <inertia ixx="0.004085980" ixy="0.000000178" ixz="-0.000000095"
+               iyy="0.007221854" iyz="0.000000526" izz="0.004086529"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/fl_wheel.STL" scale="0.001122449 0.001428571 0.001122449"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 0.064 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.12" radius="0.11"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="fl_wheel_joint" type="continuous">
+    <origin xyz="0 0 -0.300" rpy="0 0 0"/>
+    <parent link="fl_knee"/>
+    <child link="fl_wheel"/>
+    <axis xyz="0 -1 0"/>
+    <limit effort="28.68" velocity="109.956"/>
+  </joint>
+
+  <!-- Front-right.  Right-link tensors use the exact proper rotation
+       (x,y,z)_link = (z,x,y)_SolidWorks-module. -->
+  <link name="fr_knee">
+    <inertial>
+      <origin xyz="0.000040 0.001088 -0.158988" rpy="0 0 0"/>
+      <mass value="0.885"/>
+      <inertia ixx="0.013089177" ixy="0.000002705" ixz="-0.000004495"
+               iyy="0.013409459" iyz="0.000242701" izz="0.000443796"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/fr_knee.STL" scale="0.001 0.001 0.001"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 0.0005 -0.15575" rpy="0 0 0"/>
+      <geometry>
+        <box size="0.088 0.031 0.3765"/>
+      </geometry>
+    </collision>
+    <collision>
+      <origin xyz="0 0.02625 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.0205" radius="0.01995"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="fr_knee_joint" type="revolute">
+    <origin xyz="0.117577164466 -0.167 0.070" rpy="0 0 0"/>
+    <parent link="base_link"/>
+    <child link="fr_knee"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="-2.792" upper="2.809" effort="75.0" velocity="17.8024"/>
+  </joint>
+
+  <link name="fr_wheel">
+    <inertial>
+      <origin xyz="0.000002 -0.064 -0.000009" rpy="0 0 0"/>
+      <mass value="1.340"/>
+      <inertia ixx="0.004085980" ixy="0.000000178" ixz="0.000000095"
+               iyy="0.007221854" iyz="-0.000000526" izz="0.004086529"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/fr_wheel.STL" scale="0.001122449 0.001428571 0.001122449"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 -0.064 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.12" radius="0.11"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="fr_wheel_joint" type="continuous">
+    <origin xyz="0 0 -0.300" rpy="0 0 0"/>
+    <parent link="fr_knee"/>
+    <child link="fr_wheel"/>
+    <axis xyz="0 -1 0"/>
+    <limit effort="28.68" velocity="109.956"/>
+  </joint>
+
+  <link name="hl_knee">
+    <inertial>
+      <origin xyz="-0.000040 -0.001088 -0.158988" rpy="0 0 0"/>
+      <mass value="0.885"/>
+      <inertia ixx="0.013089177" ixy="0.000002705" ixz="0.000004495"
+               iyy="0.013409459" iyz="-0.000242701" izz="0.000443796"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/hl_knee.STL" scale="0.001 0.001 0.001"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 -0.0005 -0.15575" rpy="0 0 0"/>
+      <geometry>
+        <box size="0.088 0.031 0.3765"/>
+      </geometry>
+    </collision>
+    <collision>
+      <origin xyz="0 -0.02625 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.0205" radius="0.01995"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="hl_knee_joint" type="revolute">
+    <origin xyz="-0.117577164466 0.167 0.070" rpy="0 0 0"/>
+    <parent link="base_link"/>
+    <child link="hl_knee"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="-2.809" upper="2.792" effort="75.0" velocity="17.8024"/>
+  </joint>
+
+  <link name="hl_wheel">
+    <inertial>
+      <origin xyz="-0.000002 0.064 -0.000009" rpy="0 0 0"/>
+      <mass value="1.340"/>
+      <inertia ixx="0.004085980" ixy="0.000000178" ixz="-0.000000095"
+               iyy="0.007221854" iyz="0.000000526" izz="0.004086529"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/hl_wheel.STL" scale="0.001122449 0.001428571 0.001122449"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 0.064 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.12" radius="0.11"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="hl_wheel_joint" type="continuous">
+    <origin xyz="0 0 -0.300" rpy="0 0 0"/>
+    <parent link="hl_knee"/>
+    <child link="hl_wheel"/>
+    <axis xyz="0 -1 0"/>
+    <limit effort="28.68" velocity="109.956"/>
+  </joint>
+
+  <link name="hr_knee">
+    <inertial>
+      <origin xyz="0.000040 0.001088 -0.158988" rpy="0 0 0"/>
+      <mass value="0.885"/>
+      <inertia ixx="0.013089177" ixy="0.000002705" ixz="-0.000004495"
+               iyy="0.013409459" iyz="0.000242701" izz="0.000443796"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/hr_knee.STL" scale="0.001 0.001 0.001"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 0.0005 -0.15575" rpy="0 0 0"/>
+      <geometry>
+        <box size="0.088 0.031 0.3765"/>
+      </geometry>
+    </collision>
+    <collision>
+      <origin xyz="0 0.02625 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.0205" radius="0.01995"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="hr_knee_joint" type="revolute">
+    <origin xyz="-0.117577164466 -0.167 0.070" rpy="0 0 0"/>
+    <parent link="base_link"/>
+    <child link="hr_knee"/>
+    <axis xyz="0 -1 0"/>
+    <limit lower="-2.809" upper="2.792" effort="75.0" velocity="17.8024"/>
+  </joint>
+
+  <link name="hr_wheel">
+    <inertial>
+      <origin xyz="0.000002 -0.064 -0.000009" rpy="0 0 0"/>
+      <mass value="1.340"/>
+      <inertia ixx="0.004085980" ixy="0.000000178" ixz="0.000000095"
+               iyy="0.007221854" iyz="-0.000000526" izz="0.004086529"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="../meshes_solenopsis_visual/hr_wheel.STL" scale="0.001122449 0.001428571 0.001122449"/>
+      </geometry>
+    </visual>
+    <collision>
+      <origin xyz="0 -0.064 0" rpy="1.570796326795 0 0"/>
+      <geometry>
+        <cylinder length="0.12" radius="0.11"/>
+      </geometry>
+    </collision>
+  </link>
+
+  <joint name="hr_wheel_joint" type="continuous">
+    <origin xyz="0 0 -0.300" rpy="0 0 0"/>
+    <parent link="hr_knee"/>
+    <child link="hr_wheel"/>
+    <axis xyz="0 -1 0"/>
+    <limit effort="28.68" velocity="109.956"/>
+  </joint>
+
+</robot>
diff --git a/deep_robotics_model/README.md b/deep_robotics_model/README.md
new file mode 100644
index 0000000..d54bd42
--- /dev/null
+++ b/deep_robotics_model/README.md
@@ -0,0 +1,18 @@
+# Robot Models Repository
+
+This repository contains 3D models for three Deep Robotics robots: **Lite3**, **M20**, and **X30**. Each model is provided in MJCF, URDF, and USD formats. To visualize the model and do some measuring, I recommend using this [tool](https://viewer.robotsfan.com/). Just drag your folder to the web page and you can see everything.
+
+<span style="color: red;">**Note**</span>: This repository only contains low resolution files. To download the high resolution files, please use [this link](https://drive.google.com/drive/folders/1EOELXUYSBPEJeD0rUIkJxnlLMDr6IHBV?usp=sharing)
+
+
+## Model Overview
+**Note**: High resolution models are good for new simulators like isaaclab/isaacsim but old simulators like pybullet cannot open files this big.
+| Robot Model | High Resolution Image | Low Resolution Image |
+|:-----------:|:---------------------:|:--------------------:|
+| **Lite3**   | <img src="./images/lite3_high_res.png" width="200" /><br>Lite3/Lite3_urdf/urdf/Lite3_high_res.urdf | <img src="./images/lite3.png" width="200" /><br>Lite3/Lite3_urdf/urdf/Lite3.urdf |
+| **X30**     | <img src="./images/x30_high_res.png" width="200" /><br>X30/X30_urdf/urdf/X30_high_res.urdf | <img src="./images/x30.png" width="200" /><br>X30/X30_urdf/urdf/X30.urdf |
+| **M20**     | <img src="./images/m20_high_res.png" width="200" /><br>M20/M20_urdf/urdf/M20_high_res.urdf | <img src="./images/m20.png" width="200" /><br>M20/M20_urdf/urdf/M20.urdf |
+
+## Contributors
+See the [Contributors](Contributors.md) page for a list of contributors.
+
diff --git a/docs/fire_ant_prototype_specs.md b/docs/fire_ant_prototype_specs.md
new file mode 100644
index 0000000..752ad5a
--- /dev/null
+++ b/docs/fire_ant_prototype_specs.md
@@ -0,0 +1,201 @@
+# 火蚁样机参数简表
+
+本文档依据当前火蚁仿真模型与训练配置整理：
+
+- URDF: `deep_robotics_model/M20/M20_urdf/urdf/M20.urdf`
+- 执行器配置: `source/rl_training/rl_training/assets/deeprobotics.py`
+- 训练配置: `source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py`
+
+## 1. 结构形式
+
+火蚁当前为 8 自由度轮腿机器人
+## 2. 质量参数
+整机仿真总质量约：23.690 kg
+
+| 部件 | 单个质量 | 数量 | 小计 |
+| --- | ---: | ---: | ---: |
+| 机身 base_link | 15.882 kg | 1 | 15.882 kg |
+| 膝部连杆（按 P65MAX-S 质量建模） | 0.612 kg | 4 | 2.448 kg |
+| 轮毂电机与轮胎（W190-S） | 1.340 kg | 4 | 5.360 kg |
+
+## 3. 尺寸参数
+
+机身主体：
+
+
+长 x 宽 x 高 = 0.60 m x 0.32 m x 0.12 m
+
+
+轮子：
+
+
+半径 = 0.096 m
+直径 = 0.192 m
+轴向总尺寸 = 0.083 m（手册工程图标注 82.95 mm）
+```
+
+膝部连杆简化尺寸：
+
+
+主连杆 box = 0.05 m x 0.024 m x 0.20 m
+末端圆柱半径 = 0.04 m
+末端圆柱长度 = 0.09 m
+
+
+零关节角几何尺寸：
+
+```text
+轴距 = 0.60 m
+轮距 = 0.50 m
+轮心相对机身中心高度 = -0.21 m
+```
+
+训练初始姿态下，前膝为 `+0.5 rad`，后膝为 `-0.5 rad`：
+
+```text
+有效轴距约 = 0.801 m
+轮距 = 0.50 m
+机身中心接地高度约 = 0.280 m
+机身底部离地约 = 0.220 m
+```
+
+## 4. 电机与执行器参数
+
+### 膝关节电机
+
+仿真限制：
+
+```text
+型号 = P65MAX-S
+额定扭矩 = 21.3 Nm
+峰值扭矩 = 75.0 Nm
+速度上限 = 17.8024 rad/s ≈ 170 rpm
+```
+
+IsaacLab 控制参数：
+
+```text
+位置刚度 stiffness = 80.0
+阻尼 damping = 2.0
+控制延迟 = 0 ~ 5 step
+```
+
+当前 URDF 与 Isaac Lab actuator 均使用相同的峰值扭矩和速度上限。
+
+### 轮端电机
+
+仿真实际执行器限制：
+
+```text
+型号 = W190-S
+额定扭矩 = 8.7 Nm
+峰值扭矩 = 28.68 Nm
+额定点转速 = 94.457 rad/s ≈ 902 rpm
+速度上限 = 109.956 rad/s ≈ 1050 rpm
+理论空载轮缘线速度 = 10.56 m/s
+```
+
+URDF 中轮关节写有：
+
+```text
+effort = 28.68 Nm
+velocity = 109.956 rad/s
+```
+
+URDF 与 Isaac Lab actuator 的限制现已保持一致。
+
+## 5. 训练需求
+
+Stage 1 基础运动：
+
+```text
+前后速度 lin_vel_x = -2.0 ~ 2.0 m/s
+侧向速度 lin_vel_y = 0
+转向 yaw_rate = -1.5 ~ 1.5 rad/s
+```
+
+Stage 2 超支撑越障：
+
+```text
+前进速度 lin_vel_x = 0.25 ~ 1.2 m/s
+侧向速度 lin_vel_y = 0
+转向 yaw_rate = -0.6 ~ 0.6 rad/s
+```
+
+Stage 2 主要地形难度：
+
+```text
+boxes 高度 = 0.03 ~ 0.22 m
+上台阶高度 = 0.04 ~ 0.24 m
+下台阶高度 = 0.04 ~ 0.20 m
+随机粗糙高度 = 0.01 ~ 0.14 m
+```
+
+### 膝关节向内折叠限制
+
+膝关节角为 0 时膝连杆竖直向下，因此膝关节角就是腿相对竖直方向的偏角。前腿向内折叠对应关节角减小，后腿向内折叠对应关节角增大。训练要求向内最多折叠到竖直向下，不能更向内：
+
+```text
+前膝 q >= 0
+后膝 q <= 0
+```
+
+超出部分以二次惩罚计入奖励：
+
+```text
+penalty = sum(max(-q_front, 0)^2) + sum(max(q_hind, 0)^2)
+```
+
+实现位置：
+
+- 惩罚函数：`mdp/rewards.py::knee_inward_fold_penalty`
+- 奖励项与权重：`config/wheeled/deeprobotics_m20/rough_env_cfg.py` 中的 `knee_inward_fold_penalty`
+- 当前权重为 `-1.0`，`max_inward_tilt` 为 `0.0`（即允许的最大向内倾角为 0，腿不能越过竖直向下姿态）
+
+向外折叠（前膝 q > 0、后膝 q < 0）不受该惩罚约束，仍由常规关节位置限制约束。若训练中仍出现越界，可增大惩罚权重；若限制过于保守影响越障，可将 `max_inward_tilt` 调大（例如 `0.1` rad）或在阶段 2 适当减小权重。
+
+## 6. 样机设计重点
+
+优先级建议：
+
+1. 膝关节峰值扭矩优先于轮端极速。
+2. 轮端要保证低速大扭矩，避免顶障碍时堵转。
+3. 机身底部、膝部、轮架必须能承受超支撑接触冲击。
+4. 轮胎材料要保证摩擦和一定缓冲，不能只看刚性轮模型。
+5. 电机驱动器需要考虑峰值电流、连续散热和限流策略。
+
+## 7. 当前仿真限制
+
+接触材质配置：
+
+| 接触对象 | 静摩擦随机范围 | 动摩擦随机范围 | 恢复系数随机范围 |
+| --- | ---: | ---: | ---: |
+| W190-S 轮子 | 0.9 ~ 1.4 | 0.6 ~ 0.9 | 0.0 ~ 0.05 |
+| base_link 与 knee links | 0.4 ~ 0.8 | 0.25 ~ 0.4 | 0.0 ~ 0.05 |
+
+地形静、动摩擦均为 1.0，摩擦使用乘法组合，因此轮地有效摩擦范围与轮子材质随机范围一致。火蚁任务将地形恢复系数覆盖为 0.0，因此轮地碰撞的有效恢复系数为 0。质量与惯量随机化保持启用。
+
+当前模型不能直接等价为最终硬件设计：
+
+- 几何为简化 box/cylinder，不是完整 CAD。
+- 没有真实电机扭矩-转速曲线。
+- 没有电池、电流限制、热衰减。
+- 没有减速器效率、背隙、柔性结构。
+- 没有轮胎变形模型。
+- 接触与摩擦比真实世界更理想。
+- 轮关节 URDF effort 与 IsaacLab actuator effort 不一致，训练实际以 actuator 为准。
+
+## 8. 初版样机目标
+
+建议以如下指标作为第一版样机基准：
+
+```text
+整机仿真质量：约 23.69 kg
+轮径：0.192 m
+轮距：0.50 m
+有效轴距：约 0.80 m
+膝关节峰值扭矩：75 Nm
+轮端峰值扭矩：28.68 Nm
+机身底部离地：约 0.22 m
+目标越障高度：0.20 ~ 0.24 m
+```
diff --git a/docs/fire_ant_smooth_training.md b/docs/fire_ant_smooth_training.md
new file mode 100644
index 0000000..2e5999a
--- /dev/null
+++ b/docs/fire_ant_smooth_training.md
@@ -0,0 +1,561 @@
+# 火蚁平稳性训练任务文档
+
+> **任务 ID**：`Smooth-Deeprobotics-M20-v0`
+> **机器人**：火蚁 / Solenopsis（8 自由度轮腿）
+> **训练目标**：在低频随机起伏地形上保持机身 pitch / roll 水平，同时稳定跟踪线速度与偏航速度
+> **文档依据**：当前工作区代码 + 2026-10-28 两次实际运行的 `params/env.yaml`、`params/agent.yaml`、`train_smooth*.log`
+> **文档更新时间**：2026-11-02
+
+## 1. 任务定位
+
+平稳性训练是 Stage-1 粗糙地形策略之上的微调任务。动作空间、观测维度、机器人模型、质量/接触随机化都保持不变，因此可以从 Stage-1 checkpoint 直接 resume；变化集中在“地形”和“姿态平稳性奖励”两处。
+
+### 1.1 与 Stage-1（Rough）的关键差异
+
+下表按当前工作区代码对比；实际运行的参数以各运行目录下的 `params/env.yaml` 为准。
+
+| 类别 | Stage-1 Rough | 平稳性 Smooth |
+| --- | --- | --- |
+| 地形 | 台阶、箱子、随机粗糙、斜坡混合 | 100% 低频正弦平滑起伏，无台阶、无尖边 |
+| 地形初始化等级 | `max_init_terrain_level = 2` | `max_init_terrain_level = 9` |
+| 重置位姿 | x/y ±1.0 m，z 偏移 0，roll/pitch ±0.3 rad | x/y ±2.0 m，z 偏移 +0.30 m，roll/pitch ±0.05 rad |
+| 姿态惩罚 | `flat_orientation_l2 = -0.35`、`roll_l2 = -0.45` | `flat_orientation_l2` 置 0；`roll_l2 = -0.4`、`pitch_l2 = -0.4` |
+| 机身角速度惩罚 | `ang_vel_xy_l2 = -0.05` | `ang_vel_xy_l2 = -0.1` |
+
+速度指令分布（`x ∈ [-2, 2] m/s`、`y = 0`、`yaw ∈ [-1.5, 1.5] rad/s`、heading 指令开启、2% 站立环境）在两种地形下保持一致。
+
+### 1.2 平稳性由哪些设置共同保证
+
+| 机制 | 具体设置 | 作用 |
+| --- | --- | --- |
+| 俯仰惩罚 | `pitch_l2 = -0.4`，弧度平方 | 直接压低机头/机尾俯仰角 |
+| 横滚惩罚 | `roll_l2 = -0.4`，弧度平方 | 直接压低左右横滚角 |
+| 角速度阻尼 | `ang_vel_xy_l2 = -0.1` | 抑制 roll/pitch 方向的晃动速度 |
+| 竖直速度惩罚 | `lin_vel_z_l2 = -2.0` | 抑制上下弹跳 |
+| 竖直姿态奖励 | `upward = +0.08`，`(1 - g_z)²` | 机身越接近水平奖励越高（直立时 g_z = -1，项值最大） |
+| 机身高度约束 | `base_height_l2 = -0.5`，目标 0.45 m（相对机身下方地形均值） | 防止趴下或过度抬升 |
+| 膝内折约束 | `knee_inward_fold_penalty = -1.0`，`max_inward_tilt = 0.0` | 膝不能向内侧折过竖直位置 |
+| 倾倒终止 | `bad_orientation_2`，倾角超过 0.9 rad 终止 | 直接淘汰失稳策略 |
+| 地形 | 平滑正弦起伏，无台阶冲击 | 只施加连续的 pitch/roll 扰动，专练平稳性 |
+
+## 2. 相关文件
+
+| 内容 | 路径 |
+| --- | --- |
+| 任务注册 | `source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/__init__.py` |
+| 平稳性环境配置 | `.../deeprobotics_m20/smooth_env_cfg.py` |
+| 父类环境配置 | `.../deeprobotics_m20/rough_env_cfg.py` |
+| PPO 配置 | `.../deeprobotics_m20/agents/rsl_rl_ppo_cfg.py` |
+| 平地/基础环境 | `source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/velocity_env_cfg.py` |
+| 平稳地形生成 | `.../locomotion/velocity/mdp/terrain.py` |
+| 自定义奖励/观测 | `.../locomotion/velocity/mdp/rewards.py`、`observations.py` |
+| 机器人模型与执行器 | `source/rl_training/rl_training/assets/deeprobotics.py` |
+| URDF | `deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf` |
+
+## 3. 实际训练运行记录
+
+本次平稳性训练由两次运行接力完成，日志与 checkpoint 都保存在 `logs/rsl_rl/huoyi_smooth/`。
+
+### 3.1 运行 A：小地形试跑（2026-10-28_16-50-47）
+
+| 项目 | 值 |
+| --- | --- |
+| 起点 checkpoint | `/home/suyufei/syf/huoyi_training/logs/rsl_rl/deeprobotics_m20_rough/2026-10-28_10-23-40_stage1_readapt_realmass/model_24800.pt` |
+| 并行环境数 | 2048 |
+| 迭代数 | 50 |
+| GPU | `cuda:2` |
+| 地形参数 | `height_range=(0.02, 0.05)`、`wavelength_range=(1.5, 3.0)`、`max_init_terrain_level=1` |
+| 姿态奖励 | `flat_orientation_l2 = -0.8`（未拆分 roll/pitch） |
+| 结束状态 | 迭代 24849，mean reward 60.34，mean episode length 919.83 |
+| 产出 | `model_24800.pt`、`model_24849.pt` |
+
+运行 A 的目的：先在小起伏、低地形等级上确认从粗糙地形策略切换过来不会崩，同时保留一份可回退的中间策略。
+
+### 3.2 运行 B：正式平稳性训练（2026-10-28_18-16-44）
+
+| 项目 | 值 |
+| --- | --- |
+| 起点 checkpoint | `logs/rsl_rl/huoyi_smooth/2026-10-28_16-50-47/model_24849.pt` |
+| 并行环境数 | 4096 |
+| 迭代数 | 2500（从 24849 续到 27348） |
+| GPU | `cuda:0` |
+| 地形参数 | `height_range=(0.10, 0.20)`、`wavelength_range=(1.0, 2.2)`、`max_init_terrain_level=9` |
+| 姿态奖励 | `flat_orientation_l2 = 0`；`roll_l2 = -0.4`、`pitch_l2 = -0.4`；`ang_vel_xy_l2 = -0.1` |
+| 总样本量 | 245,760,000 steps（2500 × 24 × 4096） |
+| 训练耗时 | 约 2 小时 57 分（Iteration time 约 3.09–3.20 s） |
+| 最终产出 | `model_27348.pt`，以及 `exported/policy.pt`、`exported/policy.onnx` |
+
+运行 B 才是本任务的有效训练：地形起伏幅度、波长密度和初始地形等级都明显提高，逼迫策略在连续起伏上主动调平机身。
+
+### 3.3 复现命令
+
+运行 A（小地形试跑）：
+
+```bash
+python scripts/reinforcement_learning/rsl_rl/train.py \
+  --task Smooth-Deeprobotics-M20-v0 \
+  --num_envs 2048 \
+  --max_iterations 50 \
+  --headless \
+  --resume \
+  --load_run /home/suyufei/syf/huoyi_training/logs/rsl_rl/deeprobotics_m20_rough/2026-10-28_10-23-40_stage1_readapt_realmass \
+  --checkpoint /home/suyufei/syf/huoyi_training/logs/rsl_rl/deeprobotics_m20_rough/2026-10-28_10-23-40_stage1_readapt_realmass/model_24800.pt
+```
+
+运行 B（从运行 A 续训）：
+
+```bash
+python scripts/reinforcement_learning/rsl_rl/train.py \
+  --task Smooth-Deeprobotics-M20-v0 \
+  --num_envs 4096 \
+  --max_iterations 2500 \
+  --headless \
+  --resume \
+  --load_run /home/suyufei/syf/huoyi_training_level/logs/rsl_rl/huoyi_smooth/2026-10-28_16-50-47 \
+  --checkpoint /home/suyufei/syf/huoyi_training_level/logs/rsl_rl/huoyi_smooth/2026-10-28_16-50-47/model_24849.pt
+```
+
+说明：
+
+- 恢复训练时策略/价值网络输入维度必须一致（本任务 policy 33 维、critic 223 维、动作 8 维）。
+- `--checkpoint` 是脚本实际使用的参数名；日志 `params/agent.yaml` 中对应字段名为 `load_checkpoint`。
+- 不传 `--max_iterations` 时使用配置默认值 5000；上述两次运行都用命令行覆盖了默认值。
+- 环境种子默认 42，实际运行未覆盖。
+
+### 3.4 播放与导出
+
+```bash
+python scripts/reinforcement_learning/rsl_rl/play.py \
+  --task Smooth-Deeprobotics-M20-v0 \
+  --num_envs 16 \
+  --checkpoint /home/suyufei/syf/huoyi_training_level/logs/rsl_rl/huoyi_smooth/2026-10-28_18-16-44/model_27348.pt
+```
+
+## 4. 仿真与场景设置
+
+| 项目 | 值 |
+| --- | --- |
+| 物理步长 `sim.dt` | 0.005 s（200 Hz） |
+| 控制抽帧 `decimation` | 4 |
+| 控制步长 | 0.02 s（50 Hz） |
+| 回合长度 `episode_length_s` | 20 s（1000 个控制步） |
+| 并行环境数 `num_envs` | 4096（正式训练） |
+| 环境间距 `env_spacing` | 2.5 m |
+| 随机种子 | 42 |
+| 设备 | `cuda:0` |
+| 重力 | `(0, 0, -9.81)` |
+| PhysX 位置/速度迭代 | 4 / 1 |
+| CCD、稳定性增强 | 均关闭 |
+| 地形物理材质 | 静摩擦 1.0、动摩擦 1.0、恢复系数 0.0，摩擦/恢复均为 multiply |
+| 渲染间隔 | 4 个物理步 |
+| 接触传感器更新周期 | 0.005 s，历史长度 3，`track_air_time=True` |
+| 高度扫描更新周期 | 0.02 s |
+
+实测运行环境：IsaacLab 2.3.2 / Isaac Sim 5.1，8 × RTX 4090（正式训练使用 0 号卡），72 核 Xeon Platinum 8352V，约 512 GB 内存。
+
+## 5. 机器人模型
+
+| 项目 | 值 |
+| --- | --- |
+| URDF | `deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf` |
+| 自由度 | 8（4 个膝关节 + 4 个轮关节） |
+| 整机仿真质量 | 约 23.69 kg |
+| 轮半径 | 0.11 m（奖励计算同样使用 0.11） |
+| 轮距 | 0.50 m（奖励计算同样使用 0.50） |
+| 根初始高度 | (0, 0, 0.32) m |
+| 初始关节角 | 前膝 `+0.5 rad`，后膝 `-0.5 rad`，轮 `0` |
+| 软关节限位系数 | 0.9 |
+| 自碰撞 | 关闭 |
+
+### 5.1 关节顺序
+
+```text
+膝关节：fl_knee_joint, fr_knee_joint, hl_knee_joint, hr_knee_joint
+轮关节：fl_wheel_joint, fr_wheel_joint, hl_wheel_joint, hr_wheel_joint
+```
+
+### 5.2 执行器（DelayedPDActuator）
+
+| 参数 | 膝关节 | 轮关节 |
+| --- | ---: | ---: |
+| `effort_limit` | 75.0 N·m | 28.68 N·m |
+| `velocity_limit` | 17.8024 rad/s | 109.956 rad/s |
+| `stiffness` | 80.0 | 0.0 |
+| `damping` | 2.0 | 0.6 |
+| `friction` | 0.0 | 1.0 |
+| `armature` | 0.0 | 0.00243216 |
+| 动作延迟 | 0–5 个物理步 | 0–5 个物理步 |
+
+膝关节是位置控制，轮关节是纯速度控制（stiffness 为 0）。执行器的刚度、阻尼会在每次 reset 时按 0.85–1.15 缩放。
+
+## 6. 动作空间
+
+动作为 8 维，四膝位置 + 四轮速度：
+
+| 动作项 | 关节 | 缩放 | 默认偏置 | 裁剪 | 说明 |
+| --- | --- | ---: | --- | --- | --- |
+| `joint_pos` | 4 个膝关节 | 0.25 | `use_default_offset=True` | ±100 | 目标角 = 默认角 + 0.25 × 动作 |
+| `joint_vel` | 4 个轮关节 | 5.0 | 默认 0 | ±100 | 目标轮速 = 5.0 × 动作 (rad/s) |
+
+动作顺序被 `preserve_order=True` 固定，分别对应配置中的膝关节顺序与轮关节顺序，避免按正则匹配后顺序漂移导致 resume 时动作错位。
+
+## 7. 地形设置
+
+平稳性地形由 `mdp/terrain.py` 中的 `smooth_random_terrain` 生成，核心是一个“多个低频正弦波叠加”的连续高度场。
+
+### 7.1 生成公式
+
+对每个地形块：
+
+1. 在 `8 m × 8 m` 的网格上生成坐标（水平分辨率 0.05 m）；
+2. 叠加 `num_waves = 3` 个正弦波，每个波随机采样：
+   - 方向 `direction ~ U(0, 2π)`
+   - 波长 `wavelength ~ U(1.0, 2.2) m`
+   - 相位 `phase ~ U(0, 2π)`
+   - 权重 `weight ~ U(0.5, 1.0)`
+3. 减去高度均值；
+4. 按 `max(|h|)` 归一化到当前课程幅度 `amplitude`；
+5. 按 `vertical_scale = 0.005 m` 量化为 int16 高度场。
+
+课程幅度：
+
+```text
+amplitude = 0.10 + difficulty × (0.20 - 0.10)   # difficulty ∈ [0, 1]
+```
+
+即课程起点起伏峰值 0.10 m，终点 0.20 m。地形没有台阶和尖锐边缘，机器人受到的是连续的 pitch/roll 扰动。
+
+### 7.2 地形参数
+
+| 参数 | 值 |
+| --- | --- |
+| 地形块尺寸 | 8.0 m × 8.0 m |
+| 行数 × 列数 | 10 × 20 |
+| 边界宽度 / 高度 | 2.0 m / 1.0 m |
+| 水平分辨率 | 0.05 m |
+| 垂直分辨率 | 0.005 m |
+| 是否缓存 | 否（`use_cache=False`） |
+| 课程 | 开启（`curriculum=True`），难度 0 → 1 |
+| 子地形比例 | `smooth_random` = 1.0 |
+| 起伏峰值范围 | 0.10 → 0.20 m（课程起止） |
+| 波长范围 | 1.0 → 2.2 m |
+| 正弦波数量 | 3 |
+| 子地形边界宽度 | 0.25 m |
+| 初始地形等级上限 | 9（10 行中的最高等级） |
+
+### 7.3 地形课程规则
+
+使用 IsaacLab 标准的 `terrain_levels_vel`：
+
+- **升级**：本回合行走距离 > 地形块尺寸的一半（4 m）；
+- **降级**：行走距离 < 指令速度 × 20 s × 0.5；
+- 每个环境初始等级从 `[0, max_init_terrain_level]` 均匀采样。
+
+正式训练开始时平均地形等级约 4.95，结束时升至 5.96（满级 9），说明策略在持续向更难地形推进。
+
+### 7.4 地形材质
+
+| 参数 | 值 |
+| --- | --- |
+| 静摩擦 | 1.0 |
+| 动摩擦 | 1.0 |
+| 恢复系数 | 0.0 |
+| 摩擦组合方式 | multiply |
+| 恢复组合方式 | multiply |
+
+轮子材质另有随机化（见第 11 节），因此轮地有效摩擦主要由轮子材质范围决定。
+
+## 8. 观测空间
+
+采用非对称 Actor-Critic：策略只看本体感知，价值网络额外看地形高度扫描。
+
+### 8.1 Policy 观测（33 维）
+
+| 序号 | 观测项 | 维度 | 噪声 | 缩放 | 说明 |
+| ---: | --- | ---: | --- | ---: | --- |
+| 0 | `base_ang_vel` | 3 | U(-0.2, 0.2) | 0.25 | 机身角速度 |
+| 1 | `projected_gravity` | 3 | U(-0.05, 0.05) | 1.0 | 重力在机体系的投影，直接反映 pitch/roll |
+| 2 | `velocity_commands` | 3 | 无 | 1.0 | 线速度 x、y 与偏航速度指令 |
+| 3 | `joint_pos` | 8 | U(-0.01, 0.01) | 1.0 | 4 个膝相对角 + 4 个轮关节占位（轮位置恒置 0） |
+| 4 | `joint_vel` | 8 | U(-1.5, 1.5) | 0.05 | 4 膝 + 4 轮关节速度 |
+| 5 | `actions` | 8 | 无 | 1.0 | 上一步动作 |
+
+合计 3 + 3 + 3 + 8 + 8 + 8 = 33 维。
+
+**策略是“盲”的**：`base_lin_vel` 和 `height_scan` 在 policy 组中被显式置空，机器人只能依靠 IMU、关节编码器和指令来保持平稳，不能直接“看到”地形。
+
+### 8.2 Critic 观测（223 维）
+
+| 序号 | 观测项 | 维度 | 说明 |
+| ---: | --- | ---: | --- |
+| 0 | `base_lin_vel` | 3 | 机身线速度（特权信息） |
+| 1 | `base_ang_vel` | 3 | 机身角速度 |
+| 2 | `projected_gravity` | 3 | 重力投影 |
+| 3 | `velocity_commands` | 3 | 速度指令 |
+| 4 | `joint_pos` | 8 | 关节位置 |
+| 5 | `joint_vel` | 8 | 关节速度 |
+| 6 | `actions` | 8 | 上一步动作 |
+| 7 | `height_scan` | 187 | 机身下方 1.6 m × 1.0 m 地形高度网格（17 × 11，分辨率 0.1 m），裁剪到 ±1.0 |
+
+critic 组不注入噪声。合计 3 + 3 + 3 + 3 + 8 + 8 + 8 + 187 = 223 维。
+
+### 8.3 传感器
+
+| 传感器 | 配置 |
+| --- | --- |
+| `contact_forces` | 覆盖 `/World/envs/env_.*/Robot/.*`，更新周期 0.005 s，历史长度 3，记录 air time |
+| `height_scanner` | 挂在 `base_link`，从上方 20 m 向下投射，1.6 m × 1.0 m，分辨率 0.1 m，yaw 对齐，187 个点 |
+| `height_scanner_base` | 挂在 `base_link`，0.1 m × 0.1 m，分辨率 0.05 m，仅用于 `base_height_l2` 估计脚下地形高度 |
+
+## 9. 奖励设置（22 项）
+
+奖励项权重与最终训练日志值如下。`Episode_Reward` 是 IsaacLab 奖励管理器的日志量：回合累积奖励（已按 `dt = 0.02` 积分）除以 20 s，因此它与权重和回合长度都相关，只适合看趋势和相对大小。
+
+### 9.1 姿态平稳性
+
+| 奖励项 | 权重 | 关键参数 | 末次日志值 | 作用 |
+| --- | ---: | --- | ---: | --- |
+| `lin_vel_z_l2` | -2.0 | 无 | -0.1122 | 抑制机身竖直方向弹跳 |
+| `ang_vel_xy_l2` | -0.1 | 无 | -0.0815 | 抑制 roll/pitch 角速度 |
+| `roll_l2` | -0.4 | 欧拉角 roll，弧度平方 | -0.0043 | 直接惩罚横滚角 |
+| `pitch_l2` | -0.4 | 欧拉角 pitch，弧度平方 | -0.0049 | 直接惩罚俯仰角 |
+| `base_height_l2` | -0.5 | 目标高度 0.45 m，`height_scanner_base` 估计脚下地形 | -0.0208 | 维持机身高度 |
+| `upward` | +0.08 | `(1 - g_z)²`，直立时 g_z = -1 | +0.2872 | 机身越水平奖励越高 |
+
+`flat_orientation_l2` 在平稳性任务中被置 0（不参与训练），由 `roll_l2` + `pitch_l2` 取代。运行 A 及更早的设置中 `flat_orientation_l2` 为 -0.8；正式训练把它拆成 -0.4 / -0.4，便于分别调节俯仰和横滚。
+
+### 9.2 速度与偏航跟踪
+
+| 奖励项 | 权重 | 关键参数 | 末次日志值 | 作用 |
+| --- | ---: | --- | ---: | --- |
+| `track_lin_vel_xy_exp` | +2.0 | std = 0.7071（即 √0.5） | +1.6189 | 跟踪 xy 线速度 |
+| `track_ang_vel_z_exp` | +1.5 | std = 0.5 | +1.0915 | 跟踪偏航角速度 |
+| `track_diff_wheel_yaw_exp` | +1.0 | std = 0.6，轮半径 0.11，轮距 0.50，左轮 (0,2)，右轮 (1,3)，指令阈值 0.05 | +0.5449 | 用左右轮速差估计偏航，鼓励差速转向 |
+| `lin_vel_xy_yaw_command_l2` | -0.4 | 偏航阈值 0.25、线速度阈值 0.15 | -0.0012 | 原地转向时惩罚线速度漂移 |
+
+### 9.3 关节、动作与功率
+
+| 奖励项 | 权重 | 作用关节 | 末次日志值 | 作用 |
+| --- | ---: | --- | ---: | --- |
+| `joint_torques_l2` | -2.5e-5 | 4 膝 | -0.0356 | 抑制膝力矩 |
+| `joint_acc_l2` | -3e-6 | 4 膝 | -0.1570 | 抑制膝加速度 |
+| `joint_acc_wheel_l2` | -1e-7 | 4 轮 | -0.0413 | 轻微抑制轮加速度 |
+| `joint_pos_limits` | -5.0 | 4 膝 | -0.0000 | 关节限位惩罚 |
+| `joint_power` | -2e-5 | 4 膝 | -0.0014 | 抑制膝功率 |
+| `knee_joint_pos_penalty` | -0.1 | 4 膝 | -0.0751 | 站立或低速时把膝拉回默认姿态（`stand_still_scale=5`、速度阈值 0.5、指令阈值 0.1） |
+| `knee_inward_fold_penalty` | -1.0 | 前膝 / 后膝 | -0.0002 | 禁止膝向内折过竖直位置（`max_inward_tilt = 0.0`） |
+| `action_rate_l2` | -0.015 | 全部动作 | -0.1226 | 动作平滑 |
+| `stand_still` | -2.0 | 4 膝 | -0.0495 | 站立指令下关节偏离默认姿态的惩罚 |
+
+`knee_inward_fold_penalty` 的方向约定：膝关节角为 0 时膝连杆竖直向下；前膝角减小为内折，后膝角增大为内折。`max_inward_tilt = 0.0` 表示不允许越过竖直位置，向外折叠仍由关节限位约束。
+
+### 9.4 接触与轮地
+
+| 奖励项 | 权重 | 关键参数 | 末次日志值 | 作用 |
+| --- | ---: | --- | ---: | --- |
+| `undesired_contacts` | -1.0 | 除轮子外的所有 body，力阈值 1.0 N | -0.0002 | 禁止机身、膝等部位触地拖行 |
+| `contact_forces` | -1.5e-4 | 4 个轮子，阈值 100 N | -0.0166 | 抑制轮地过大冲击力 |
+| `feet_contact_without_cmd` | +0.1 | 4 个轮子 | +0.0069 | 无速度指令时保持轮子着地 |
+
+### 9.5 被关闭的奖励项
+
+以下奖励项权重为 0，训练时被移除或置空：`is_terminated`、`flat_orientation_l2`、`body_lin_acc_l2`、`joint_vel_l2`、`joint_vel_wheel_l2`、`joint_torques_wheel_l2`、`joint_vel_limits`、`feet_air_time`、`feet_air_time_variance`、`feet_contact`、`feet_stumble`、`feet_slide`、`feet_height`、`feet_height_body`、`feet_distance_y_exp`、`feet_gait`、`wheel_vel_penalty`、`joint_mirror`、`action_mirror`、`action_sync`、`applied_torque_limits`、`joint_deviation_l1`、`stand_still_without_cmd`、`joint_pos_penalty`、`hipx_joint_pos_penalty`、`hipy_joint_pos_penalty`、`command_progress`、`support_contact_progress`、`support_contact_excess`。
+
+注意：平稳性任务没有 `illegal_contact` 终止，机身/膝触地不会立即结束回合，而是通过 `undesired_contacts` 持续扣分；这与 Stage-2 允许短时支撑的设计不同。
+
+## 10. 终止条件
+
+| 终止项 | 参数 | `time_out` | 说明 |
+| --- | --- | --- | --- |
+| `time_out` | 20 s | 是 | 正常回合结束 |
+| `terrain_out_of_bounds` | 距地形块原点 3.0 m 缓冲 | 是 | 跑出当前地形块，按超时处理，不计为摔倒 |
+| `bad_orientation_2` | 倾角上限 0.9 rad（约 51.6°） | 否 | 机身过度倾斜，判定失稳 |
+| `illegal_contact` | — | — | 已禁用，机身/膝触地不终止 |
+
+`bad_orientation_2` 使用 `mdp.bad_orientation`，比较机身 z 轴与重力方向的夹角。它和 `roll_l2`、`pitch_l2` 形成“软惩罚 + 硬终止”的双层约束。
+
+## 11. 速度指令
+
+| 参数 | 值 |
+| --- | --- |
+| 指令类 | `UniformThresholdVelocityCommand` |
+| 重采样周期 | 10 s |
+| `lin_vel_x` | -2.0 ~ 2.0 m/s |
+| `lin_vel_y` | 0.0（不允许横移） |
+| `ang_vel_z` | -1.5 ~ 1.5 rad/s |
+| heading 指令 | 开启，`heading_control_stiffness = 0.5` |
+| heading 范围 | -π ~ π |
+| 站立环境比例 | 2% |
+| heading 环境比例 | 100% |
+
+`UniformThresholdVelocityCommand` 会在采样后把平面线速度模长 ≤ 0.2 m/s 的指令清零，因此低速线速度指令会被视为 0；偏航速度不受此阈值影响。这保证了“要么有明显前进/后退，要么原地转向”，减少模棱两可的小速度指令。
+
+## 12. 域随机化设置
+
+所有事件均由父类 `EventCfg` 继承，平稳性任务未额外修改（除重置位姿在 `smooth_env_cfg.py` 中覆盖）。
+
+| 事件 | 模式 | 对象 | 参数 |
+| --- | --- | --- | --- |
+| `randomize_rigid_body_mass` | startup | 除 `base_link` 外的所有 body | 质量 scale 0.85–1.15，重算惯量 |
+| `randomize_rigid_body_mass_base` | startup | `base_link` | 质量 add -1.0 ~ +3.0 kg，重算惯量 |
+| `randomize_rigid_body_inertia` | startup | 所有 body | 惯量 scale 0.85–1.15 |
+| `randomize_com_positions` | startup | `base_link` | COM x/y ±0.03 m，z ±0.02 m |
+| `randomize_apply_external_force_torque` | reset | `base_link` | 力 ±10 N，力矩 ±10 N·m |
+| `randomize_reset_joints` | reset | 全部关节 | 位置 scale 1.0–1.0（等同不随机），速度 0 |
+| `randomize_actuator_gains` | reset | 4 膝 + 4 轮 | 刚度、阻尼均 scale 0.85–1.15，均匀分布 |
+| `randomize_reset_base` | reset | 机器人根 | 见下方重置位姿 |
+| `randomize_push_robot` | interval 10–15 s | 机器人根 | x/y 速度各 ±0.5 m/s |
+| `randomize_wheel_material` | startup | `.*_wheel` | 静摩擦 0.9–1.4，动摩擦 0.6–0.9，恢复 0–0.05，1024 桶 |
+| `randomize_support_material` | startup | `base_link`、`.*_knee` | 静摩擦 0.4–0.8，动摩擦 0.25–0.4，恢复 0–0.05，1024 桶 |
+| `randomize_rigid_body_material` | — | 全部 body | 已禁用，由上面两个材质事件替代 |
+
+### 12.1 重置位姿
+
+| 轴 | 范围 |
+| --- | --- |
+| x | -2.0 ~ 2.0 m（相对默认根位置偏移） |
+| y | -2.0 ~ 2.0 m（相对默认根位置偏移） |
+| z | +0.30 m（相对默认根高度的偏移） |
+| roll | -0.05 ~ 0.05 rad |
+| pitch | -0.05 ~ 0.05 rad |
+| yaw | -π ~ π |
+| 线速度 x | -0.1 ~ 0.1 m/s |
+| 其余速度 | 0 |
+
+注意：IsaacLab 的 `reset_root_state_uniform` 把 `z` 当作**相对默认根状态的偏移**。机器人 `init_state` 的根高度是 0.32 m，因此重置时实际高度约为 0.32 + 0.30 = 0.62 m（相对地形原点），会先经历一次落地再进入正常控制。若希望原地生成，应把 z 改为 0.0。
+
+## 13. PPO 超参数
+
+| 参数 | 值 |
+| --- | --- |
+| Runner | `OnPolicyRunner`（RSL-RL） |
+| 策略结构 | ActorCritic，MLP 512-256-128，ELU |
+| Actor 输入 / 输出 | 33 / 8 |
+| Critic 输入 / 输出 | 223 / 1 |
+| 初始噪声标准差 | 1.0，`noise_std_type="log"` |
+| `num_steps_per_env` | 24（单环境 rollout 0.48 s） |
+| 每轮样本数 | 24 × 4096 = 98,304 |
+| `max_iterations` | 2500（命令行覆盖；配置默认 5000） |
+| `save_interval` | 100 |
+| `num_learning_epochs` | 5 |
+| `num_mini_batches` | 4（mini-batch 24,576 样本） |
+| 学习率 | 1e-3，adaptive schedule，`desired_kl = 0.01` |
+| 折扣 / GAE | `gamma = 0.99`，`lam = 0.95` |
+| PPO clip | 0.2 |
+| 熵系数 | 0.01 |
+| 价值损失系数 | 1.0，使用 clipped value loss |
+| 梯度裁剪 | 1.0 |
+| 观测归一化 | 关闭（`empirical_normalization=False`） |
+| 动作裁剪 | ±100 |
+| 日志 | TensorBoard |
+| 实验名 | `huoyi_smooth` |
+| 随机种子 | 42 |
+
+单轮耗时约 3.1–3.2 s，采集速度约 8.8k steps/s，2500 轮合计约 2 小时 57 分。
+
+## 14. 训练结果（运行 B，迭代 27348）
+
+| 指标 | 值 |
+| --- | --- |
+| 有效迭代 | 2500（24849 → 27348） |
+| 总样本量 | 245,760,000 steps |
+| 训练耗时 | 2 小时 57 分 08 秒 |
+| 平均回合奖励 | 55.95 |
+| 平均回合长度 | 886.40 步（约 17.73 s / 20 s） |
+| 平均地形等级 | 5.96 / 9 |
+| 线速度跟踪误差 `error_vel_xy` | 0.3823 m/s |
+| 偏航速度跟踪误差 `error_vel_yaw` | 0.4167 rad/s |
+| `time_out` 终止比例 | 80.10% |
+| `terrain_out_of_bounds` 比例 | 18.89% |
+| `bad_orientation_2` 比例 | 1.04% |
+| 价值损失 | 0.0929 |
+| 策略替代损失 | 0.0028 |
+| 熵损失 | 7.4557 |
+| 平均动作噪声标准差 | 0.68 |
+
+### 14.1 奖励分解（末次日志）
+
+正向项合计约 +3.55，负向项合计约 -0.72，主要贡献来自：
+
+| 奖励项 | 末次日志值 |
+| --- | ---: |
+| `track_lin_vel_xy_exp` | +1.6189 |
+| `track_ang_vel_z_exp` | +1.0915 |
+| `track_diff_wheel_yaw_exp` | +0.5449 |
+| `upward` | +0.2872 |
+| `feet_contact_without_cmd` | +0.0069 |
+| `joint_acc_l2` | -0.1570 |
+| `action_rate_l2` | -0.1226 |
+| `lin_vel_z_l2` | -0.1122 |
+| `ang_vel_xy_l2` | -0.0815 |
+| `knee_joint_pos_penalty` | -0.0751 |
+| `stand_still` | -0.0495 |
+| `joint_acc_wheel_l2` | -0.0413 |
+| `joint_torques_l2` | -0.0356 |
+| `base_height_l2` | -0.0208 |
+| `contact_forces` | -0.0166 |
+| `pitch_l2` | -0.0049 |
+| `roll_l2` | -0.0043 |
+| 其他 | 均接近 0 |
+
+### 14.2 关于 resume 首轮的“异常低值”
+
+运行 B 的第一条日志（迭代 24849）显示 mean reward -0.94、mean episode length 12.99。这是 resume 时 `init_at_random_ep_len=True` 造成的统计瞬态：所有环境被随机放到回合计时器的任意位置，第一轮 rollout 内瞬间完成的“残余回合”被计入均值，并不代表策略崩溃。后续迭代迅速恢复到正常运行区间。
+
+### 14.3 与运行 A 的对比
+
+| 指标 | 运行 A（小地形） | 运行 B（正式） |
+| --- | ---: | ---: |
+| 并行环境 | 2048 | 4096 |
+| 地形峰值 | 0.02–0.05 m | 0.10–0.20 m |
+| 初始地形等级上限 | 1 | 9 |
+| 平均奖励 | 60.34 | 55.95 |
+| 平均回合长度 | 919.83 | 886.40 |
+
+运行 A 的奖励更高，但地形明显更容易，两者不能直接比较；运行 B 是在更难的起伏地形和更高初始等级下取得的 55.95。
+
+## 15. 调参要点与注意事项
+
+1. **地形难度要分级推进。** 运行 A（0.02–0.05 m、初始等级 1）先确认策略不崩，运行 B 再切到 0.10–0.20 m、初始等级 9。直接从大起伏开始容易出现早期大量 `bad_orientation`。
+2. **roll / pitch 分开调。** 平稳性任务把原来的 `flat_orientation_l2` 拆成 `roll_l2` 与 `pitch_l2`，当前各 -0.4；如果某一轴仍晃动，可单独加大该轴权重，而不会同时把另一轴压得过死。
+3. **角速度惩罚不要太小。** `ang_vel_xy_l2` 从 Stage-1 的 -0.05 提到 -0.1，是抑制“角度不大但持续晃”的关键项。
+4. **resume 时不要改观测/动作维度或顺序。** 当前契约为 policy 33 维、critic 223 维、动作 8 维，且膝关节顺序被 `preserve_order=True` 固定；改顺序会导致动作错位但训练不报错。
+5. **`base_height_l2` 的 0.45 m 是相对 `height_scanner_base` 所测脚下地形均值的高度。** 修改该扫描器的尺寸或分辨率会改变高度目标的实际含义。
+6. **重置 z 是偏移量。** 当前 z = +0.30 m 是叠加在默认根高度 0.32 m 上的偏移，实际约 0.62 m；若不需要落地过程，应改为 0.0。
+7. **奖励里的轮半径 0.11 m、轮距 0.50 m 要与 URDF 同步。** `track_diff_wheel_yaw_exp` 用这两个常数把轮速差换算成偏航角速度，URDF 改轮径后必须同步修改。
+8. **`undesired_contacts` 阈值只有 1.0 N。** 机身、膝几乎一碰地就会扣分，这是“不允许趴地爬行”的强约束；若发现策略过度保守，可先检查这一项。
+9. **`terrain_out_of_bounds` 占 18.89%。** 它被标记为 `time_out=True`，不算摔倒，但会截断回合。8 m × 8 m 地形块配合最高 2 m/s 的指令，高速直行时容易跑出边界；若希望提高有效回合长度，可增大块尺寸或 `distance_buffer`。
+10. **策略是盲的。** `base_lin_vel` 和 `height_scan` 只在 critic 中；部署时不需要地形感知，但也意味着策略完全依赖 IMU 与关节反馈来保持平稳。
+11. **控制频率与延迟。** 50 Hz 控制、执行器 0–5 个物理步延迟（0–25 ms），仿真里的平稳性结论不能直接等同于实机，实机部署前应确认延迟与电机响应。
+12. **不要用运行 A 的奖励值判断平稳性优劣。** 两次运行的地形分布不同，平均奖励不可直接横比。
+
+## 16. 产物与日志
+
+| 内容 | 路径 |
+| --- | --- |
+| 运行 A 目录 | `logs/rsl_rl/huoyi_smooth/2026-10-28_16-50-47/` |
+| 运行 B 目录 | `logs/rsl_rl/huoyi_smooth/2026-10-28_18-16-44/` |
+| 最终 checkpoint | `logs/rsl_rl/huoyi_smooth/2026-10-28_18-16-44/model_27348.pt` |
+| 导出策略 | `.../2026-10-28_18-16-44/exported/policy.pt`、`policy.onnx` |
+| 运行参数 | `.../2026-10-28_18-16-44/params/env.yaml`、`agent.yaml` |
+| TensorBoard | `.../2026-10-28_18-16-44/events.out.tfevents.*` |
+| 代码快照 | `.../2026-10-28_18-16-44/git/IsaacLab-2.3.2.diff`、`huoyi_training_level.diff` |
+| 运行 A 日志 | `train_smooth50.log` |
+| 运行 B 日志 | `train_smooth4096_1028_1816.log` |
+| 未进入训练的启动日志 | `train_smooth4096_1028_1812.log`（启动阶段退出，未产生迭代） |
+
+checkpoint 命名与迭代号对应：`model_24900.pt` … `model_27348.pt`，每 100 轮保存一次，最后一次为 27348。
+
+## 17. 可选的后续实验
+
+以下仅为建议，未在本次训练中执行：
+
+1. 把地形峰值从 0.20 m 提高到 0.25 m，或把波长下限继续降到 0.8 m，观察 `bad_orientation` 与 `error_vel_xy` 的变化。
+2. 针对 `terrain_out_of_bounds` 偏高的问题，尝试 10 m × 10 m 地形块或更大的 `distance_buffer`，减少高速直行被截断。
+3. 在保持 policy 观测不变的前提下，尝试把 `ang_vel_xy_l2` 从 -0.1 调到 -0.15，观察横滚/俯仰速度是否进一步下降。
+4. 单独统计 roll、pitch 的 RMS 与峰值，作为“平稳性”的量化指标，而不是只看总奖励。
+5. 若后续 URDF 轮径或轮距变化，同步更新 `track_diff_wheel_yaw_exp` 的 `wheel_radius`、`track_width`，并重新校准 `base_height_l2` 的目标高度。
+
+---
+
+**文档维护**：修改 `smooth_env_cfg.py`、`rough_env_cfg.py`、`terrain.py`、`rewards.py` 或 PPO 配置后，请同步更新本文对应章节，并在“实际训练运行记录”中追加新的运行目录与关键指标。
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diff --git a/docs/motor_model_update_comparison.md b/docs/motor_model_update_comparison.md
new file mode 100644
index 0000000..ba41986
--- /dev/null
+++ b/docs/motor_model_update_comparison.md
@@ -0,0 +1,51 @@
+# P65MAX-S / W190-S 模型更新对比
+
+数据来源：智身科技《关节选型手册 2026》。本次将简化 M20 URDF 的关节电机替换为 P65MAX-S，将轮毂电机替换为 W190-S。
+
+## 关节与执行器
+
+| 参数 | 修改前 | 修改后 |
+| --- | ---: | ---: |
+| 膝关节峰值扭矩 | 76.4 Nm | 75.0 Nm |
+| 膝关节速度上限 | 22.4 rad/s（约 214 rpm） | 17.8024 rad/s（170 rpm） |
+| 单个 knee link 质量 | 1.000 kg | 0.612 kg |
+| 轮关节 URDF 扭矩上限 | 40.0 Nm | 28.68 Nm |
+| 轮关节训练执行器扭矩上限 | 21.6 Nm | 28.68 Nm |
+| 轮关节速度上限 | 79.3 rad/s（约 757 rpm） | 109.956 rad/s（1050 rpm） |
+
+## 轮毂几何与质量
+
+| 参数 | 修改前 | 修改后 |
+| --- | ---: | ---: |
+| 轮半径 | 0.140 m | 0.096 m |
+| 轮直径 | 0.280 m | 0.192 m |
+| 轴向尺寸 | 0.090 m | 0.083 m |
+| 单轮质量 | 1.400 kg | 1.340 kg |
+| 绕轮轴转动惯量 | 0.013700 kg·m² | 0.006174720 kg·m² |
+| 径向转动惯量 | 0.007500 kg·m² | 0.003856632 kg·m² |
+| 空载理论轮缘速度 | 11.10 m/s | 10.56 m/s |
+
+W190-S 的新转动惯量按质量 1.34 kg、半径 0.096 m、轴向尺寸 0.083 m 的均匀实心圆柱重新计算。
+
+## 整机影响
+
+| 参数 | 修改前 | 修改后 | 变化 |
+| --- | ---: | ---: | ---: |
+| URDF 总质量 | 25.482 kg | 23.690 kg | -1.792 kg |
+| 初始生成高度 | 0.350 m | 0.306 m | -0.044 m |
+| 初始姿态稳定后机身中心高度 | 约 0.324 m | 约 0.280 m | -0.044 m |
+| 初始姿态机身底部离地 | 约 0.264 m | 约 0.220 m | -0.044 m |
+| 轮距 | 0.500 m | 0.500 m | 不变 |
+| 有效轴距 | 约 0.801 m | 约 0.801 m | 不变 |
+
+## 同步修改位置
+
+- `deep_robotics_model/M20/M20_urdf/urdf/M20.urdf`：质量、惯量、轮子几何、关节限制。
+- `source/rl_training/rl_training/assets/deeprobotics.py`：训练执行器限制和初始生成高度。
+- `source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py`：差速转向奖励和轮滑观测所用轮半径。
+
+## 建模边界
+
+- 简化 URDF 没有独立的电机刚体，因此将 knee link 质量直接设为 P65MAX-S 的 0.612 kg，并按质量比例缩放原 knee link 惯量。
+- W190-S 手册给出的是约 83 mm 的轴向总尺寸，简化圆柱模型将其作为轮宽。
+- 当前执行器仍使用固定扭矩与固定速度上限，没有拟合手册中的完整扭矩—转速曲线、热衰减或电流限制。
diff --git a/docs/solenopsis_urdf_comparison.md b/docs/solenopsis_urdf_comparison.md
new file mode 100644
index 0000000..6b90b7b
--- /dev/null
+++ b/docs/solenopsis_urdf_comparison.md
@@ -0,0 +1,114 @@
+# Solenopsis CAD URDF 与旧 M20 简化 URDF 对比
+
+对比对象：
+
+- 旧模型：`deep_robotics_model/M20/M20_urdf/urdf/M20.urdf`
+- 新模型：`deep_robotics_model/M20/M20_urdf/urdf/Solenopsis.urdf`
+- CAD 来源：`Solenopsis-00 火蚁-四单臂轮足平台总成.STEP`
+
+## 质量闭合
+
+用户提供的刚体分组质量为：机身组 17.274 kg、单个摆臂组 0.955 kg、单个轮组 1.340 kg；整机质量为 26.581 kg。
+
+```text
+17.274 + 4 × 0.955 + 4 × 1.340 = 26.454 kg
+26.581 - 26.454 = 0.127 kg
+```
+
+按确认结果，将差额 0.127 kg（即 127 g，不是 0.127 g）合并到 `base_link`，因此新 `base_link` 质量为 17.401 kg。该差额等效放在已报告的机身组质心处，所以不改变机身组质心坐标和惯量张量；没有假定未知零部件的空间分布。
+
+| 刚体 | 旧模型单体质量 | 新模型单体质量 | 单体变化 |
+| --- | ---: | ---: | ---: |
+| `base_link` | 15.882 kg | 17.401 kg | +1.519 kg（+9.5643%） |
+| 每个 `*_knee` | 0.612 kg | 0.955 kg | +0.343 kg（+56.0458%） |
+| 每个 `*_wheel` | 1.340 kg | 1.340 kg | 0 |
+| 整机 | 23.690 kg | 26.581 kg | +2.891 kg（+12.2035%） |
+
+## 坐标换算
+
+SolidWorks 总装坐标为 Y 轴向上；现有训练模型采用 ROS/URDF 的 Z 轴向上。新模型使用以下右手坐标变换：
+
+```text
+(x, y, z)_URDF = (z, x, y)_SolidWorks
+```
+
+摆臂和轮子的质量属性原始坐标系是运动模组坐标系，X 轴为电机旋转轴，Y 轴沿轮心指向关节中心。为了保持现有控制器的关节符号约定，新 URDF 统一定义：
+
+- 四个关节轴均为 `(0, -1, 0)`；
+- `q = 0` 时四个摆臂均竖直向下；
+- 关节中心到轮轴中心的距离为 CAD 中的精确值 0.300 m；
+- 左右侧采用不同的正规旋转矩阵变换质量属性，没有使用镜像矩阵变换惯量。
+
+## 质心与惯量
+
+所有新惯量均为“关于各自刚体质心、并与对应 URDF link 坐标轴对齐”的惯量，单位为 kg·m²。SolidWorks 的 kg·mm² 已严格乘以 `10^-6`，并按 `I_URDF = R I_SW R^T` 旋转。
+
+### base_link
+
+| 参数 | 旧模型 | 新模型 |
+| --- | ---: | ---: |
+| 质心 x | 0.003551059 m | -0.000072 m |
+| 质心 y | 0.001650758 m | 0.000171 m |
+| 质心 z | -0.003464621 m | 0.076892 m |
+| Ixx | 0.088078700 | 0.133964838 |
+| Iyy | 0.533816000 | 0.363696493 |
+| Izz | 0.563915000 | 0.450717599 |
+| Ixy | 0.001396000 | -0.000732466 |
+| Ixz | 0.000111000 | 0.000157104 |
+| Iyz | 0.000519000 | 0.000165399 |
+
+### 单个 knee
+
+新左侧 knee 质心为 `(-0.000040, -0.001088, -0.158988) m`，新右侧 knee 质心为 `(0.000040, 0.001088, -0.158988) m`。
+
+| 参数 | 旧模型代表值 | 新模型左侧 | 新模型右侧 |
+| --- | ---: | ---: | ---: |
+| Ixx | 0.005202000 | 0.014124479 | 0.014124479 |
+| Iyy | 0.005202000 | 0.014470094 | 0.014470094 |
+| Izz | 0.000734400 | 0.000478899 | 0.000478899 |
+| Ixy | ±0.000024480 | 0.000002919 | 0.000002919 |
+| Ixz | ±0.000091800 | 0.000004851 | -0.000004851 |
+| Iyz | ±0.000673200 | -0.000261898 | 0.000261898 |
+
+### 单个 wheel
+
+新左轮质心为 `(-0.000002, 0.059326, -0.000009) m`，新右轮质心为 `(0.000002, -0.059326, -0.000009) m`。这保留了 SolidWorks 报告的轮组轴向质心偏置，不再把轮组质心强制放在关节原点。
+
+| 参数 | 旧均匀圆柱模型 | 新模型左侧 | 新模型右侧 |
+| --- | ---: | ---: | ---: |
+| Ixx | 0.003856632 | 0.003296552 | 0.003296552 |
+| Iyy（轮轴） | 0.006174720 | 0.005826562 | 0.005826562 |
+| Izz | 0.003856632 | 0.003296995 | 0.003296995 |
+| Ixy | 0 | 0.000000144 | 0.000000144 |
+| Ixz | 0 | -0.000000077 | 0.000000077 |
+| Iyz | 0 | 0.000000424 | -0.000000424 |
+
+## 关节几何
+
+| 参数 | 旧简化模型 | 新 CAD 模型 |
+| --- | ---: | ---: |
+| 前/后关节中心 x | ±0.300 m | ±0.117577164466 m |
+| 左/右关节中心 y | ±0.140 m | ±0.167 m |
+| 关节中心 z | 0 m | 0.070 m |
+| 膝关节至轮轴向量 | `(0, ±0.110, -0.210) m` | `(0, 0, -0.300) m` |
+| 膝关节至轮轴距离 | 0.237065 m | 0.300000 m |
+| 关节轴 | `(0, -1, 0)` | `(0, -1, 0)` |
+| 自由度 | 8 | 8 |
+
+关节名称、顺序、扭矩上限和速度上限均保持不变，因此软件接口仍是四个 knee 加四个 wheel。不过，质量、质心、惯量、关节位置和杆长都有实质变化，旧策略不能被视为动力学等价策略，应重新训练或至少重新做系统性验证。
+
+## 显示与碰撞几何
+
+新 URDF 使用从 STEP 总成拆分得到的 9 个 STL 作为 visual：一个机身、四个摆臂、四个轮组。STL 保留毫米坐标，URDF 使用 `0.001` 比例换算到米。当前训练引用 `meshes_solenopsis_visual` 中的可视化网格；高精度原始导出保留在 `meshes_solenopsis` 中。
+
+collision 不使用 STL，只使用方块和圆柱，尺寸来自 CAD 外廓：
+
+- `base_link`：一个 `0.706 × 0.310 × 0.1714 m` 方块；
+- 每个 `*_knee`：一个 `0.088 × 0.031 × 0.3765 m` 摆臂方块，加一个 P65 转动端圆柱；
+- 每个 `*_wheel`：半径 `0.098804 m`、轴向宽度 `0.082959 m` 的圆柱。
+
+训练可视化网格约 23.5 万个三角形、11.7 MB，顶点相对高精度 STL 的最大量化位移不超过 1.733 mm。非闭合边、内部电气件和紧固件不会参与碰撞求解。这样既保留 CAD 外观，又避免 214 万面高精度 STL 拖慢 Isaac Sim 导入，同时让并行训练继续使用稳定的基础几何碰撞体。
+
+## 当前切换状态
+
+旧 `M20.urdf` 没有被覆盖，IsaacLab训练入口已经切换到 `Solenopsis.urdf`。导入时，visual加载STEP拆分得到的STL用于正常显示，collision只加载方块和圆柱参与物理计算。
diff --git a/eval_kit/README.md b/eval_kit/README.md
new file mode 100644
index 0000000..f598c4f
--- /dev/null
+++ b/eval_kit/README.md
@@ -0,0 +1,561 @@
+# eval_kit —— 可搬运的机器人性能评价套件
+
+**这份套件与 `scripts/reinforcement_learning/rsl_rl/eval.py` 并存，不导入也不修改它。**
+两者的指标名完全一致、取点一致、公式一致，所以输出可以直接互相对照。
+
+## 为什么要另写一套
+
+`eval.py` 是一个能跑通的评估脚本，但它把三件事混在一起：读仿真环境、算指标、格式化输出。
+其中「读环境」必须知道具体机器人长什么样——它写死了 `.*_wheel` 和 `["base_link", ".*_knee"]`。
+于是它只能评估火蚁；而且因为 IsaacLab 的 `find_bodies` 对匹配不到的正则会直接抛
+`ValueError`，换一个没有轮子的机器人跑会在进循环之前就崩，不是给出一个错的分数。
+
+本套件把三层切开，让不依赖框架的部分可以单独测试、单独搬运：
+
+```
+eval_kit/
+├── core/                   ★ 零框架依赖（只用 numpy + 标准库），可整块拷到别的项目
+│   ├── schema.py           │  RobotProfile / EvalProtocol / Trace 三个数据契约
+│   ├── metrics.py          │  指标纯函数（唯一算数的地方）
+│   ├── aggregation.py      │  上限网格、score、跨种子 mean±std
+│   ├── terrain_spec.py     │  地形等级规格（纯数据）
+│   ├── report.py           │  格式化与指标筛选
+│   └── naming.py           │  容错版的名字/正则解析
+├── isaaclab/               × 依赖 isaaclab，唯一接触仿真内部的地方
+│   ├── env_setup.py        │  把协议与地形规格翻译成 env_cfg 改动
+│   ├── recorder.py         │  跑 rollout 并采集成 Trace
+│   └── policy.py           │  checkpoint 解析与策略加载
+├── profiles.py             ★ 机器人形态描述：加机器人 = 加一条记录
+├── protocols.py            ★ 评估协议预设：改口径 = 改这里
+└── tests/
+    ├── test_core.py        ★ 合成轨迹测试指标（不需要仿真）
+    └── test_cli.py         ★ 命令行参数与口径覆盖自检（不需要仿真）
+```
+
+打 ★ 的是「可套用」的部分：`core/`、`profiles.py`、`protocols.py` 与两个测试文件
+可以在没有 Isaac Sim、没有 GPU 的机器上运行。将来要接别的框架（legged_gym、mujoco），
+只需要再写一个适配层产出 `Trace`，`core/` 一行都不用改。
+
+## 用法
+
+### 1. 激活火蚁项目的虚拟环境
+
+```bash
+cd ~/syf/IsaacLab-2.3.2
+source env_isaaclab_py311/bin/activate
+cd ~/syf/huoyi_training            # 评估脚本在仓库根目录跑
+```
+
+验证环境是否正确：`python -c "import isaaclab"` 应当无报错，且 `isaaclab.__file__`
+指向 `IsaacLab-2.3.2/source/isaaclab/isaaclab/__init__.py`（可编辑安装）。
+
+注意有两个容易踩空的近邻环境：系统 `python3`（3.10）和 conda 的
+`env_rl_training` 都**不能**跑——前者没有 isaaclab/isaacsim，后者有 isaacsim 5.1
+但没有 isaaclab。只有 `env_isaaclab_py311` 是完整的。仓库根目录是必须的，
+因为 `logs/` 查找用的是相对 cwd 的路径。
+
+### 2. 让 IsaacLab 的日志目录可写
+
+`/tmp/isaaclab` 属于机器上的其它用户，默认会 `PermissionError`。
+`configure_logging` 用的是 `tempfile.gettempdir()/isaaclab/logs`，而 `gettempdir()`
+尊重 `TMPDIR`，所以设一下即可（不必去动别人的目录）：
+
+```bash
+export TMPDIR=/home/suyufei/tmp
+```
+
+### 3. 跑评估
+
+两条硬性约束，先看：
+
+- **必须传 `--checkpoint`**（绝对路径最省事）。编排进程不启动仿真，因此不加载
+  hydra，无法按 `--load-run` 用实验名查找权重。
+- **每个种子一个子进程**。IsaacLab 在本配置下**一个进程只能创建一个环境**：
+  第一个环境建好后，`ROUGH_TERRAINS_CFG` 这类模块级单例会被运行时长出自引用，
+  之后任何 cfg 构造（新实例、开场存的 pristine 深拷贝、重新深拷贝）都会在
+  `cfg.validate()` 里 RecursionError —— 三种补救方式都实测失败。所以
+  `eval_suite.py` 的编排进程自己不建环境，而是为每个（阶段, 种子）fork 一个子进程。
+  代价是每个子进程要重新启动一次 Isaac Sim（约 15 s），所以
+  `--protocol standard_v1_multiseed`（5 个种子）大约要 5~10 分钟。
+
+```bash
+# 标准评估（默认用 solenopsis 描述 + standard_v1 协议）
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --headless
+
+# 上限评估 + 5 个随机种子 + 存轨迹
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --protocol standard_v1_multiseed --save-trace /tmp/traces --headless
+
+# 抗扰评估（每 2.01 s 施加一次水平扰动）
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --eval-type robustness --headless
+
+# 只看某一种地形的上限
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --eval-type upper_limits --terrain-type stairs_up --headless
+
+# 推荐用于机器人对比：放大地砖，消除越界终止项的误判（见下一节）
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --protocol standard_v1_clean --headless
+
+# 或者直接关掉那个终止项
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --no-terrain-bounds --headless
+
+# 冒烟测试（小规模，只为确认流水线通）
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_19999.pt \
+    --protocol smoke --headless
+```
+
+输出默认写到 `<checkpoint 目录>/eval_kit/<eval_type>_<时间戳>.json`——刻意与
+`eval.py` 的 `<checkpoint 目录>/eval/` 分开，避免两套结果混在一个目录里。
+
+快速自检（不需要仿真）：
+
+```bash
+python3 eval_kit/tests/test_core.py    # 24 项：指标、聚合、名字解析、轨迹存取
+python3 eval_kit/tests/test_cli.py     # 19 项：参数解析、口径覆盖、路径规则、推流开关
+```
+
+### 4. 用 WebRTC 看画面（可视化）
+
+机器上没有显示器时，用 IsaacLab 的推流模式看评估过程：加 `--livestream` 即可
+（这个参数由 `AppLauncher` 提供，`eval_suite.py` 自己没有定义）。
+
+```bash
+# 局域网直连（推荐）：客户端和服务器在同一网段
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 \
+    --checkpoint /绝对路径/model_18200.pt \
+    --protocol smoke --terrain-type stairs_up \
+    --livestream 2 --device cuda:6
+```
+
+- `--livestream 2`：局域网/私有网络 WebRTC，客户端直连 `<本机 IP>:49100`。
+- `--livestream 1`：公网 WebRTC，走 NVIDIA NVCF 信令，需要 `PUBLIC_IP` 且能出公网。
+- 两个值都会让 `AppLauncher` 自动进入 headless（推流由服务端渲染），
+  **不要再传 `--headless`**（传了也无害，只是多余）。
+
+客户端用 NVIDIA 的 **Isaac Sim WebRTC Streaming Client**（随 Isaac Sim 一起提供，
+Windows/Linux 都有），填入服务器地址即可；端口默认 49100，脚本启动时会把实际
+地址打印出来：
+
+```
+[eval_kit] WebRTC 推流已启用（--livestream 2，局域网直连）
+[eval_kit]   客户端连接地址：10.62.25.137:49100  192.168.77.26:49100
+```
+
+脚本为推流额外做三件事：
+
+1. **视口相机默认跟着被评估的机器人**。`--view-env N` 选第 N 个环境（默认 env 0），
+   上限评估时会打印这个编号对应哪种地形、哪个难度等级。不设的话推流画面停在
+   默认相机上，看不到机器人。
+   想自己控制镜头就加 **`--free-camera`**：改为不跟随任何物体，起始摆在俯视全场的
+   位置（按实际地形范围算中心与半径），之后在客户端里用鼠标/键盘自由移动 ——
+   Isaac Lab 的 `ViewportCameraController` 只在 `origin_type="asset_root"` 时每帧
+   改写相机，设成 `world` 就只在开头写一次，不会把你的操作抢回去。同时给了
+   `--view-env` 时以 `--free-camera` 为准。
+2. **开始 rollout 前留出连接时间**：`--pause-s`（秒），推流时默认 30 s，
+   期间**物理被冻结**、画面照常刷新，在终端按回车可提前开始。不推流时默认 0。
+   已验证：停顿与否跑出来的指标逐位相同（同一 seed）。
+   注意这一窗口里机器人是**悬空静止**的：它停在协议规定的起始位姿上，而
+   `standard_v1_clean` 的 `start_z_offset_m=0.5` 会把它抬高 0.5 m 再落下，
+   所以「离地没落下、也不动」正是正常现象；`sim.play()` 之后才开始动。
+   启动时会打印倒计时，别把它当成卡死。
+3. **打印客户端连接地址**（从 carb 设置里读实际端口，不是写死的）。
+
+**想看清机器人走路**：`smoke`/`standard_v1` 一类协议一个 episode 只有 2~10 s 仿真时间，
+扣掉 30 s 等待后留给你看的时间很短。只为观察的话把 episode 拉长（这会改变 `protocol`
+指纹，**别拿这些数字做对比**）：
+
+```bash
+# 只看画面：60 s 的 episode、60 个机器人，混合地形上一直走
+python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 --checkpoint /绝对路径/model_18200.pt \
+    --protocol standard_v1_clean --eval-type velocity_error \
+    --episode-length-s 60 --num-envs 60 \
+    --livestream 2 --view-env 0 --pause-s 30 --device cuda:0
+```
+
+**每个（阶段, 种子）是一个独立子进程**，各自开一次推流；子进程退出时画面中断，
+下一个阶段会自动重新推流，客户端重连即可。所以 `--protocol standard_v1_multiseed`
+这种 8 个种子的协议会看到 8 次「断线—重连」。想连续观察就用单种子的协议
+（`standard_v1` / `smoke`），或者按地形单独跑。
+
+**这块机器上跑推流要先挑一张空闲卡**。渲染 + 编码要占显存，而渲染路径（MGPU/
+流编码器）会在 GPU 0 上申请资源，GPU 0 正被别人的训练占满时会刷大量
+`Out of GPU memory` / `Buffer creation failed for the device: 0`，严重时
+`CUDA error: an illegal memory access` 直接把评估打断。处置：
+
+```bash
+nvidia-smi --query-gpu=index,memory.used --format=csv   # 挑一张空卡
+--device cuda:N                                         # 渲染与物理都钉到 N
+--rendering_mode performance                            # 可选：低画质换显存与延迟
+--kit_args=--/renderer/multiGpu/maxGpuCount=1           # 可选：限制渲染只用一张卡
+```
+
+`--device` 现在同时决定**策略网络**放在哪张卡：以前策略只认 agent 配置里的
+`device`（默认 `cuda:0`），在别人占着 0 号卡时会莫名其妙 OOM。
+
+**读数提醒（未解）**：同一 checkpoint、同一协议、同一 seed，**启动方式不同会得到不同的
+数值**。实测（`--protocol smoke --eval-type velocity_error --num-envs 64`，同一个
+`model_18200.pt`）：
+
+| 启动方式 | mean_lin_vel_error | mean_yaw_vel_error | 中途早退 |
+| --- | --- | --- | --- |
+| `--headless --device cuda:6`（三次独立重跑） | 0.8159 | 0.1480 | 10 |
+| `CUDA_VISIBLE_DEVICES=6 --headless --device cuda:0` | 0.8507 | 0.3334 | 17 |
+| `--headless --enable_cameras`（渲染开，无窗口） | 0.8507 | 0.3334 | 17 |
+| `--livestream 2 --device cuda:6`（推流，暂停 0~30 s 都试过） | 0.8507 | 0.3334 | 17 |
+
+每一组内部都是**逐位可复现**的（同组三次重跑完全一致），跨组则有稳定差异；`--pause-s`
+已验证与差异无关（headless 加暂停与不加完全相同；推流下暂停 0 s 与 30 s 也相同）。
+两档之间的成因尚未定位（与「是否渲染」和「`--device` 写法/可见卡数」都相关，未能归因到单一因素）。
+**在这个问题查清之前：拿数字做对比时固定一种启动方式**，不要把可视化那次的数值与历史
+headless 结果混在一个表里报。看画面用推流没有影响，只是它的数字属于另一档。
+
+**画面卡住时先看终端，不要猜**。rollout 期间每 10% 打印一行进度：
+
+```
+[eval_kit] 停顿 5.0 s 结束，开始 rollout（画面从这里才动）
+[eval_kit] rollout 100/1000 步，用时 7.0 s
+[eval_kit] rollout 200/1000 步，用时 13.6 s
+...
+[eval_kit] 结果已保存：.../upper_limits_2026-10-19_17-16-19.json
+```
+
+- **有 `rollout` 打印在涨** → 仿真在正常推进，卡的是画面（渲染/编码侧）。按优先级试：
+  换到空闲卡（`--device cuda:N`，N 用空闲卡的编号）、
+  `--rendering_mode performance`、确认客户端没断线、机器人数量少一点（`--num-envs`）。
+  已知一种具体成因已经默认修掉：客户端改窗口大小/全屏会发 `onCustomResizeMessage`，
+  服务端默认拒绝并让推流的 `shared swapchain buffer` 进入坏状态，此后画面不再更新
+  （仿真还在跑）。脚本现在会给推流自动追加
+  `--/app/livestream/allowResize=true --/app/livestream/allowDynamicResize=true`。
+  如果日志里仍出现 `onCustomResizeMessage ... must both be positive`，**别缩放客户端窗口**，
+  并把那段日志发出来。
+- **`rollout` 打印不再涨** → 是仿真停了，通常是在客户端 UI 里动了时间轴/仿真控件：
+  视口下方的时间轴 Play/Pause/Stop 按钮、IsaacLab 面板里的 `Record Animation`
+  （START 之后默认 10 s 结束会**直接关掉应用**）、`Reset Simulation on Stop`。
+  看画面时别点它们；真被误触了就 Ctrl-C 重跑。
+- **进程已经结束**（终端出现 `结果已保存`，或被 Ctrl-C）→ 客户端只会把最后一帧留在屏幕上，
+  看起来就是「画面定住」，这既不是卡住也没有在算，重跑即可。
+- 一轮跑完的标志只有两个：终端出现 `结果已保存：<路径>.json`，以及
+  `<checkpoint 目录>/eval_kit/` 下多出一个 JSON。
+
+### 已知问题：WebRTC 画面在客户端接上后停止更新
+
+实测到过这个现象：仿真、渲染、相机都正常（`--record-frames` 抓出的帧显示机器人在动、
+相机跟着走），**只有推流给客户端的画面不再刷新**。已排除的原因：物理冻结（终端
+`rollout` 进度一直在涨）、暂停窗口（停顿结束后仍不动）、客户端改画幅被拒（已默认打开
+`allowResize`/`allowDynamicResize`）、时间轴被暂停（那样进度会停）。
+
+推流器默认把日志静音（`app.livestream.logLevel = "silence"`），所以要先让它说话：
+
+```bash
+--kit_args=--/app/livestream/logLevel=debug
+# 另外可以试：--kit_args=--/renderer/multiGpu/maxGpuCount=1
+# （IsaacLab 自己的 isaaclab.python.rendering.kit 就是这么设的，多卡机器上更保险）
+```
+
+再不行就用 `--record-frames` 那条路径看评估过程（功能上等价，只是不能实时移动相机）。
+
+**推流不可用时的替代路径：抓帧成视频。** `--record-frames DIR` 会在 rollout 期间按
+间隔把视口画面存成 PNG，跑完打印合成命令：
+
+```bash
+CUDA_VISIBLE_DEVICES=7 python scripts/reinforcement_learning/rsl_rl/eval_suite.py \
+    --task Rough-Deeprobotics-M20-v0 --checkpoint /绝对路径/model_18200.pt \
+    --protocol standard_v1_clean --eval-type velocity_error \
+    --episode-length-s 30 --num-envs 16 \
+    --livestream 2 --pause-s 0 --view-env 0 --device cuda:0 \
+    --record-frames /tmp/eval_frames --record-every 5
+```
+
+抓帧依赖视口渲染，所以**必须开着渲染**：推流（`--livestream 2`）或本地 GUI 都行，
+纯 headless（`--headless` 且不推流）没有视口，抓不到帧。跑完合成
+（`--record-every 5` 时每帧 0.1 s 仿真时间，10 fps 即按仿真速度播放）：
+
+```bash
+cd /tmp/eval_frames && ffmpeg -y -framerate 10 -pattern_type glob -i '*.png' \
+  -pix_fmt yuv420p -c:v libx264 -vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' /tmp/eval.mp4
+```
+
+已实测：600 步抓到 102 张 1280×720 PNG，合出的 mp4 能正常播放，画面里机器人从
+起始高度落地并在地形上移动、相机跟着它走 —— 这条路径绕开了推流传输层，
+在推流卡住时仍然能看评估过程。
+
+## 三个对象
+
+- **`RobotProfile`**（`profiles.py`）：这个机器人谁是足端、谁是支撑体、哪些关节耗电、多重。
+  评价代码里没有任何 link/joint 名字或质量常数。
+- **`EvalProtocol`**（`protocols.py`）：一次评估的全部口径——指令、时长、成功距离、
+  倾角阈值、加速度阈值、能耗约定、地形规格、种子。决定数值的字段会被哈希成
+  `protocol_fingerprint` 写进输出 JSON。
+- **`Trace`**（`core/schema.py`）：一次 rollout 的逐帧记录（机身位姿/速度、关节力矩与速度、
+  足端位置、支撑接触力与时长）。第 0 帧是动作前的起始状态，之后每步一帧。
+
+流水线是单向的：适配器产 `Trace` → 内核算指标 → 报告层格式化。
+`Trace` 可以存成 `.npz`，事后加新指标不用重跑仿真。
+
+## 加一个机器人
+
+在 `profiles.py` 加一条记录即可，评估代码不动：
+
+```python
+MY_ROBOT = RobotProfile(
+    name="my_robot",
+    mass_kg=None,                          # None = 运行时从资产读真实质量
+    feet_body_patterns=(".*_foot",),
+    support_body_patterns=(),              # 没有支撑体语义 → 相关指标返回 nan
+    actuated_joint_patterns=(".*_joint",),
+    support_force_threshold_n=15.0,
+    body_length_m=0.5,
+)
+```
+
+`test_core.py::test_naming_does_not_crash_when_morphology_is_missing` 就是在验证这件事：
+用火蚁的描述去解析一个没有轮子的机器人，得到的是「未命中」记录 + 跳过对应指标，而不是崩溃。
+
+## 相对 `eval.py` 的三处有意差异
+
+指标名、取点、公式全部保持一致，只有三处刻意不同：
+
+| 项 | `eval.py` | 本套件 | 原因 |
+|---|---|---|---|
+| COT 的质量 | `--robot_mass` 默认 25.482 | 默认从 `root_physx_view.get_masses()` 读（`Solenopsis.urdf` 实为 26.581 kg），命令行可覆盖 | 旧默认值是更早的 M20 简化模型名义质量，会让 COT 偏低约 4% |
+| 加速度阈值 | `--acc_threshold` 2.0 m/s²（绝对值） | `acc_threshold_g` 重力倍数（默认仍是 2.0/9.81） | 绝对值对不同量级的机器人不公平 |
+| 缺失形态的指标 | 返回 0.0 | 返回 `nan`，并在 JSON 里记录 `stability_margin_method="unavailable"` | 0.0 看起来像测出来的值，实际是没测 |
+
+另外新增了两项检查：输出 JSON 里带 `protocol_fingerprint`（防止不同口径的结果被悄悄比较），
+以及 `early_resets`（环境在 episode 中途被重置时会警告，因为那会让路程/进度类指标混入两次 rollout）。
+
+## 口径与 `criterion.py` 的对照（备忘）
+
+`criterion.py`（来自 legged_gym 的山猫项目）与 `eval.py` 在 7 处口径上不一致，
+移植/对照时要注意。已经核对过的结论：
+
+| 项 | `criterion.py` | `eval.py` / 本套件 |
+|---|---|---|
+| 能耗积分 | `Σ(τ·q̇)·dt`（有符号） | `Σ\|τ·q̇\|·dt`（绝对值，本套件可切换但默认绝对值） |
+| COT 质量 | 写死 27.78822 | 自动读取（26.581 kg） |
+| 倾角阈值 | π/6 ≈ 0.524 rad | 0.70 rad |
+| 稳定裕度 | ZMP 到足端凸包距离（scipy + shapely） | 机身到足端包围盒距离 |
+| 成功判定 | env 维护目标区域 | 沿指令方向前进 ≥ success_distance |
+| 时间效率基准 | 走过半块地形的时间 | 走完 success_distance 的时间 |
+| 上限 score | 累积成功率加权 | **代数等价，已验算一致** ✓ |
+
+其中「能耗积分」这一条最要紧：有符号口径下负功会抵消正功，同一段运动算出的能耗可以
+差一倍以上，所以跨机器人比较时必须在协议里钉死一种。
+
+## ⚠️ 发现的问题：越界终止项让约 20% 的环境从第 0 步就被重置
+
+这是实跑冒烟测试时发现的，**是 `eval.py` 评估设置里既有的问题，不是本套件引入的**。
+
+IsaacLab 的 `terrain_out_of_bounds` 终止项（`isaaclab_tasks/.../mdp/terminations.py`）
+比较的是机器人的**世界坐标**与**整张地图**的范围，而不是机器人自己那块地砖：
+
+```python
+map_width = n_rows * tile + 2 * border_width
+x_out_of_bounds = |root_pos_w[:, 0]| > 0.5 * map_width - distance_buffer
+```
+
+在 `eval.py` 的默认评估配置下（地砖 2.0 m、`border_width` 2.0、`distance_buffer` 3.0、
+起始位姿 x = −0.9），网格最外侧格子中心在 ±(rows−1)/2 × 2.0 处，机器人起始位置因此
+越过阈值。实测数据（8 难度 × 2 地形 × 每格 2 个 = 32 个环境）：
+
+- 9 行格子中有 2 个整格越界（origin x = −7.0 → 起始 x = −7.9 > 阈值 7.0）
+- **首步就有 6/32 = 19% 的环境被判越界并重置**
+- 5×5 网格下同理：最左一整列（20%）从第 0 步起每步都被重置
+
+还有第二个机制：`|y|` 恰好**等于**阈值的那些列（y = ±4.0 对阈值 4.0）处于浮点边界上，
+取决于最后一个 bit 是否触发。
+
+两个后果，都影响可比性：
+
+1. 这些环境永远没有机会尝试地形，却全部被记成失败 → 成功率和 score 系统性偏低。
+2. 地形行序是**打乱**的（哪一行落在最外侧每次都不同）→ **被清零的是哪个难度等级
+   跨种子都不稳定**，所以逐等级成功率和 score 连自己跟自己比都不可靠。
+
+### 三种处置（按推荐顺序）
+
+| 方案 | 做法 | 效果 |
+|---|---|---|
+| 1 | `--terrain-size 4.0` 或 `--protocol standard_v1_clean` | 保留终止项，消除误判。阈值由 `minimum_terrain_size_for_bounds()` 给出：`tile >= 2*(0.9 + 3.0 − 2.0) = 3.8 m` |
+| 2 | `--no-terrain-bounds` 或 `--protocol standard_v1_no_bounds` | 直接去掉该终止项。成功判定本就按前进距离算，走出地砖不构成失败 |
+| 3 | 保持默认（2.0 m 地砖 + 启用终止项） | 与旧 `eval.py` 数值可比，但带着上述偏差；只建议用于对照 |
+
+### 因果验证
+
+同一配置（5×5 网格、10 个环境、8 步、零动作策略），只切换该终止项：
+
+| `enforce_terrain_bounds` | 各环境中途重置次数 | 结论 |
+|---|---|---|
+| `True`（默认，与 eval.py 一致） | `[7, 1, 0, 0, 0, 0, 0, 7, 7, 0]` | 4/10 环境受污染 |
+| `False` | `[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]` | 无早退 |
+
+本套件的 `early_resets` 检查就是为发现这类问题加的，输出 JSON 里一直带着它。
+
+## ⚠️ 发现的问题二、三：地形参数与生成位置
+
+下面两处同样是实跑时发现的，也都继承自 ``eval.py`` 的评估设置。
+
+### 问题二：机器人在倒金字塔台阶地形里被生成到几何体内部
+
+旧写法把重置高度钉在瓦片中心的 ``env_origin.z`` 上（``pose_range.z = (0, 0)``），
+这只对「瓦片中心是最高点」的地形成立。
+
+``stairs_down`` 用的 ``MeshInvertedPyramidStairsTerrainCfg`` **中心是最低点**，
+再叠加 ``platform_width`` 被从 3.0 m 压到 0.4 m（``min(0.4, tile*0.2)``），
+起始点 x = −0.9 m 就落在平台之外且低于该处地表 —— 机器人被生成在几何体内部、卡住。
+
+结果是 ``stairs_down`` **在全部 8 个难度上都是 0.00**，连只有 4 cm 台阶的 L1 也是，
+看上去像「这机器人不会下台阶」，实际是 harness 的问题。
+
+**修复**：``EvalProtocol.start_z_offset_m``（CLI ``--start-z-offset``）。
+实测对照（同一 checkpoint、只跑 ``--terrain-type stairs_down``）：
+
+| 起始高度 | score | 成功率 |
+|---|---|---|
+| 0.0（旧 eval.py） | 0.00 / 8 | 0.05 |
+| 0.3 m | 3.64 / 8 | 0.47 |
+| **0.5 m** | **4.53 / 8** | — |
+
+取 0.5 m 是因为嵌入深度随难度递增（L1 约 0.11 m、L8 约 0.50 m），0.5 刚好覆盖最深一级。
+``standard_v1_clean`` 已内置该值；``standard_v1`` 保持 0.0 以维持与旧 eval.py 的可比性。
+代价是机器人在平台上方落下会弹一下，``stairs_up`` 略降，但整体是净收益。
+
+### 问题三：粗糙地形的噪声参数被当成起伏幅度
+
+``rough_slope`` 用的 ``HfRandomUniformTerrainCfg`` 有两个容易误解的点，IsaacLab 源码里
+写得很清楚但很容易忽略：
+
+1. **``difficulty`` 参数被忽略**（docstring 原文：*"The difficulty parameter is ignored
+   for this terrain"*）。所以那一列**没有难度梯度**，8 行是同一分布的重复采样 ——
+   这也正是它能被看出来的原因：它的成功率不单调（0.12/0.00/0.38/0.25/0.62/0.75/0.12/0.00）。
+2. **``noise_range`` 是绝对高度范围，不是起伏幅度**。旧 eval.py 设成 ``(0.05, 0.40)``，
+   而 ``noise_step`` 又被改成 0.01 —— 在 1 cm 水平网格上放 40 cm 高的随机起伏，
+   等于一堵堵随机尖刺墙，轮半径只有 0.096 m 的机器人无法通过。
+
+**修复**：``noise_step`` 回到上游的 0.02，``noise_range`` 的上限改为 0.10（与训练地形
+``noise_range=(0.02, 0.10)`` 同量级）。实测：
+
+| ``noise_range`` | score | 成功率 |
+|---|---|---|
+| (0.05, 0.40)（旧 eval.py）+ noise_step 0.01 | 0.12 / 8 | 0.05 |
+| (0.02, 0.10) + noise_step 0.02 | **6.38 / 8** | 0.86 |
+
+同时给规格加了 ``isaaclab_extra_params``（写与等级无关的固定字段），并把裸 ``setattr``
+换成带 ``hasattr`` 校验的 ``_set_sub_terrain_field``：字段名写错现在会直接报错，
+不会再静默造一个假属性、让地形保持默认难度（那看起来只是「很难」）。
+
+``rough_slope`` 的 ``unit`` 里也写明了「此地形忽略难度参数，等级排名无意义」，
+所以这条限制会随输出 JSON 一起保存。
+
+### 三处修正的合计效果
+
+同一 checkpoint（``model_19999.pt``、seed 0），只切换协议预设：
+
+| 地形 | 旧 eval.py 口径 | ``standard_v1_clean`` |
+|---|---|---|
+| stairs_up | 2.88 | 3.03 |
+| stairs_down | 0.00 | **4.21** |
+| smooth_slope | 6.50 | 6.50 |
+| rough_slope | 0.12 | **6.03** |
+| boxes | 4.28 | 4.60 |
+| **总成功率** | 0.434 | **0.659** |
+| **平均前进距离** | 3.59 m | **5.26 m** |
+| **COT** | 4.50 | **2.45** |
+| **score 总和** | 13.78 | **24.38** |
+
+## ⚠️ 尚存的未解异常：所有地形的 L8 都是 0.00
+
+修正后在完整评估里观察到：**五个地形在最高难度 L8 上的成功率全部是 0.00**。
+
+对 ``stairs_up`` / ``boxes`` 这类台阶高度确实随难度递增的地形，L8 失败是合理的；
+但 ``rough_slope`` 忽略难度参数，L8 的统计分布与 L1–L7 完全相同（那 7 行是 0.75–1.00），
+却精确地得到 0.00 —— 这在统计上不可能是能力问题，指向**最后一行的地形原点或生成高度有问题**
+（最可能是 ``terrain_origins`` 最后一行的 z 或布局异常，使机器人被放在地形之外/之下）。
+
+影响：每个地形的 ``score`` 末项 ``acc[-1] * num_levels`` 因 ``acc[-1] = 0`` 而丢失，
+score 被低估约 1 分（以 smooth_slope 为例，6.50 → 若 L8 正常约 7.40）。
+
+**这一条尚未定位，也没有修。** 在有明确结论之前，请把矩阵最后一行的数据当作不可用，
+score 当作保守下界。
+
+## 验证状态（请如实看待）
+
+**已在真机（`IsaacLab-2.3.2/env_isaaclab_py311` + RTX 4090）上实跑验证**：
+
+- `eval_kit/isaaclab/` 三个模块已端到端跑通，三步冒烟全部 PASS：
+  - `names` —— 形态解析在真实 body 名上命中：足端 4 个
+    `['fl_wheel','fr_wheel','hl_wheel','hr_wheel']`、支撑体 5 个
+    `['base_link','fl_knee',...,'hr_knee']`、驱动关节 8 个，全部与描述文件一致
+  - `plain` —— 配置 → 采集 → 指标全链路：11 帧 × 4 环境，指标齐出
+    （`margin=0.1667 m`、`max_tilt=0.051 rad`、`energy=8.0572 J`、`COT=2.1570`）
+  - `upper` —— 上限网格链路：8 个环境按 2 类型 × 2 等级分格，矩阵与 score 正确折叠
+- **整机质量从资产读出为 26.581 kg**，与 `docs/solenopsis_urdf_comparison.md` 的记录一致；
+  这确认了 `eval.py` 的 `--robot_mass` 默认值 25.482 确实偏低约 4%，本套件改为自动读取
+  是有必要的。
+- 越界终止项的偏差已定位、量化并做过因果验证（见上一节）。
+- `eval_kit/core/` 的指标用合成轨迹验证，期望值手算：22 项测试通过。
+- `eval_suite.py` 的参数解析与口径覆盖用桩替换 `isaaclab.app` 后测试：11 项通过。
+
+**仍未验证**：
+
+- 没有用**训练到收敛的** checkpoint 跑过，所以指标数值本身没有和 `eval.py` 做过逐项对照。
+  现有的 `model_0.pt` 是 iteration 0（基本等于随机权重），跑出来的 `success_rate` 恒为 0、
+  `time_consumption` 恒为 nan，拿这种退化值对照没有信息量——**这时"通过"是弱证据，
+  "报错"才是强证据**，而报错部分已经全部排除。
+- 因此**第一次拿到训练好的 checkpoint 时仍建议做等价性验证**：同一 checkpoint、
+  同一组参数分别跑 `eval.py` 与 `eval_suite.py --protocol standard_v1`，除「相对 eval.py
+  的三处有意差异」一节列出的三项外应逐位相同。注意 `eval.py` 目前**无法启动**（见下）。
+- `profiles.py` 里的 `lite3` 记录没有在仿真里跑过。
+
+## ⚠️ `eval.py` 目前无法启动
+
+实跑时顺带发现的：`eval.py` 自己也加了一个 `--verbose`，而
+`AppLauncher.add_app_launcher_args()` 会无条件添加同名参数，argparse 因此在**构造
+parser 阶段**就抛错，脚本连 `--help` 都跑不出来：
+
+```
+argparse.ArgumentError: argument --verbose: conflicting option string: --verbose
+```
+
+`--device` 和 `--verbose` 是两次踩到的坑，但只有 `device / headless / enable_cameras /
+experience / livestream / rendering_mode / xr` 这 7 个会被 AppLauncher 自己的显式检查
+拦下并给出可读报错；`--verbose / --info / --cpu` 等其余 7 个由 argparse 拦。
+本套件把 14 个名字都记在 `tests/test_cli.py::APPLAUNCHER_ARG_NAMES` 里，并在
+`test_parser_compatible_with_app_launcher` 中复刻检查，所以这类冲突不必启动仿真就能发现。
+
+修 `eval.py` 只需把那行 `--verbose` 改名（例如 `--verbose-terrain`）并同步
+`args_cli.verbose` 的用法。**我没有改动 `eval.py`**——按你的要求新写一套、不碰原文件。
+
+## 已知限制
+
+- **一个进程只能建一个环境**（IsaacLab 在本配置下的限制，实测确认）。因此
+  `eval_suite.py` 用「编排进程 + 每（阶段, 种子）一个子进程」的结构，每个子进程
+  重新启动一次 Isaac Sim。这不是本套件的设计偏好，是绕不过去的约束。
+- **地形规格是唯一真正不可移植的部分。** 规格数值（等级 × 类型 × 绝对高度）是纯数据，
+  但「把规格翻译成地形生成器」的代码是 IsaacLab 专用（`isaaclab/env_setup.py`）。
+  换框架需要另写这个 builder。
+- **稳定裕度口径较粗。** 当前是「机身到足端轴对齐包围盒的距离」，比 ZMP/凸包版本粗糙，
+  但不会在退化多边形上失败、也不需要 scipy。算法名记录在输出 JSON 里。
+- **重复试验只覆盖随机种子。** `aggregate_runs` 给的是种子间的 mean±std，
+  不含 checkpoint 之间的方差（同一实验不同迭代的权重差异）——那个要靠多取几个 checkpoint 评估。
+- **单次 rollout 的噪声很大。** 比较两个策略/机器人时请用 `--seeds` 给多个种子，
+  看 mean±std，不要看单次数值。
\ No newline at end of file
diff --git a/eval_kit/__init__.py b/eval_kit/__init__.py
new file mode 100644
index 0000000..06fc066
--- /dev/null
+++ b/eval_kit/__init__.py
@@ -0,0 +1,35 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""可搬运的机器人性能评价套件。
+
+分层：
+
+- :mod:`eval_kit.core`      —— 零框架依赖（只用 numpy + 标准库），可整体拷到别的项目
+- :mod:`eval_kit.isaaclab`  —— IsaacLab 适配层，唯一接触仿真环境内部的地方
+- :mod:`eval_kit.profiles`  —— 机器人形态描述（加机器人 = 加一条记录）
+- :mod:`eval_kit.protocols` —— 评估协议预设
+
+命令行入口：``scripts/reinforcement_learning/rsl_rl/eval_suite.py``。
+"""
+
+from eval_kit.core.aggregation import aggregate_runs, build_upper_limit_matrix
+from eval_kit.core.metrics import compute_metrics
+from eval_kit.core.schema import EvalProtocol, RobotProfile, Trace
+from eval_kit.core.terrain_spec import get_terrain_spec
+from eval_kit.profiles import PROFILES, get_profile
+from eval_kit.protocols import PROTOCOLS, get_protocol
+
+__all__ = [
+    "EvalProtocol",
+    "PROFILES",
+    "PROTOCOLS",
+    "RobotProfile",
+    "Trace",
+    "aggregate_runs",
+    "build_upper_limit_matrix",
+    "compute_metrics",
+    "get_profile",
+    "get_protocol",
+    "get_terrain_spec",
+]
diff --git a/eval_kit/core/__init__.py b/eval_kit/core/__init__.py
new file mode 100644
index 0000000..709f27f
--- /dev/null
+++ b/eval_kit/core/__init__.py
@@ -0,0 +1,26 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""评价内核：不依赖 isaaclab / isaacsim / torch / GPU，只依赖 numpy 与标准库。
+
+这里的模块可以在任何机器上 import 和测试，包括没有仿真环境的机器。
+"""
+
+from eval_kit.core.aggregation import aggregate_runs, build_upper_limit_matrix
+from eval_kit.core.metrics import compute_metrics
+from eval_kit.core.naming import NameResolution, resolve_matching_indices
+from eval_kit.core.schema import EvalProtocol, RobotProfile, Trace
+from eval_kit.core.terrain_spec import TERRAIN_SPECS, get_terrain_spec
+
+__all__ = [
+    "TERRAIN_SPECS",
+    "EvalProtocol",
+    "NameResolution",
+    "RobotProfile",
+    "Trace",
+    "aggregate_runs",
+    "build_upper_limit_matrix",
+    "compute_metrics",
+    "get_terrain_spec",
+    "resolve_matching_indices",
+]
diff --git a/eval_kit/core/aggregation.py b/eval_kit/core/aggregation.py
new file mode 100644
index 0000000..84f1e37
--- /dev/null
+++ b/eval_kit/core/aggregation.py
@@ -0,0 +1,212 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""上限评估的网格汇总：把逐环境结果折叠成「难度等级 × 地形类型」的成功率矩阵。
+
+纯数据变换，只依赖 numpy，与仿真框架无关。
+
+``score`` 的公式与 ``criterion.py`` / 旧 ``eval.py`` 完全一致（三者代数等价）：
+对每个地形类型，先算逐级累积成功率 ``acc[i] = Π_{j<=i} rate[j]``，
+再算 ``score = Σ_i acc[i] * (i+1) * (1 - rate[i+1])``，最后一级的 ``(1 - rate)`` 取 1。
+含义是「在爬到第 i+1 级之前失败」的累积期望，值越大说明越能爬到高难度。
+"""
+
+from __future__ import annotations
+
+from typing import Any, Sequence
+
+import numpy as np
+
+from eval_kit.core.terrain_spec import TerrainSpec
+
+
+def accumulated_success(rates: Sequence[float]) -> list[float]:
+    """逐级累积成功率。"""
+    accumulated: list[float] = []
+    running = 1.0
+    for rate in rates:
+        running *= float(rate)
+        accumulated.append(running)
+    return accumulated
+
+
+def upper_limit_score(rates: Sequence[float]) -> float:
+    """由某一地形类型各难度等级的成功率算 score。"""
+    rates = [float(rate) for rate in rates]
+    accumulated = accumulated_success(rates)
+    score = 0.0
+    for index, acc_rate in enumerate(accumulated):
+        next_failure = 1.0 - rates[index + 1] if index + 1 < len(rates) else 1.0
+        score += acc_rate * (index + 1) * next_failure
+    return score
+
+
+def aggregate_runs(runs: Sequence[tuple[int, dict[str, Any]]]) -> dict[str, Any]:
+    """把多次（不同随机种子）的指标聚合成 mean/std。
+
+    单次 rollout 的噪声足以淹没两个策略之间的真实差异，所以做对比时应该跑多个
+    种子并看分布，而不是看单个数。
+
+    Args:
+        runs: ``(seed, metrics)`` 列表，``metrics`` 来自
+            :func:`eval_kit.core.metrics.compute_metrics`。
+    """
+    if not runs:
+        raise ValueError("没有任何一次 run 可以聚合。")
+
+    # 逐环境的私有字段不参与聚合
+    metric_keys: list[str] = []
+    for _, metrics in runs:
+        for key, value in metrics.items():
+            if key.startswith("_"):
+                continue
+            if isinstance(value, (int, float)) and key not in metric_keys:
+                metric_keys.append(key)
+
+    summary: dict[str, Any] = {}
+    for key in metric_keys:
+        values = np.array([float(metrics.get(key, float("nan"))) for _, metrics in runs], dtype=np.float64)
+        finite = values[np.isfinite(values)]
+        if finite.size == 0:
+            summary[key] = {
+                "mean": float("nan"),
+                "std": float("nan"),
+                "min": float("nan"),
+                "max": float("nan"),
+                "values": values.tolist(),
+                "finite_runs": 0,
+            }
+            continue
+        summary[key] = {
+            "mean": float(np.mean(finite)),
+            "std": float(np.std(finite, ddof=1)) if finite.size > 1 else 0.0,
+            "min": float(np.min(finite)),
+            "max": float(np.max(finite)),
+            "values": values.tolist(),
+            "finite_runs": int(finite.size),
+        }
+
+    consistent: dict[str, Any] = {}
+    for key in runs[0][1]:
+        if not key.startswith("_") and isinstance(runs[0][1][key], str):
+            values = {metrics.get(key) for _, metrics in runs}
+            consistent[key] = runs[0][1][key]
+            if len(values) > 1:
+                consistent[f"{key}_INCONSISTENT"] = sorted(str(value) for value in values)
+
+    return {
+        "num_runs": len(runs),
+        "seeds": [int(seed) for seed, _ in runs],
+        "summary": summary,
+        "constant": consistent,
+        "per_run": [{"seed": int(seed), "metrics": metrics} for seed, metrics in runs],
+    }
+
+
+def build_upper_limit_matrix(
+    per_env: dict[str, list[float]],
+    level_ids: np.ndarray,
+    type_ids: np.ndarray,
+    terrain_types: Sequence[str],
+    spec: TerrainSpec,
+    num_levels: int,
+) -> dict[str, Any]:
+    """把逐环境结果汇总成网格。
+
+    Args:
+        per_env: 含 ``success`` / ``max_progress`` / ``max_tilt`` 的逐环境列表，
+            由 :func:`eval_kit.core.metrics.compute_metrics` 在
+            ``include_per_env=True`` 时给出。
+        level_ids: ``(N,)`` 每个环境所属的难度等级（0 起）。
+        type_ids: ``(N,)`` 每个环境所属的地形类型序号（对应 ``terrain_types``）。
+        terrain_types: 地形类型名，同时决定矩阵列顺序。
+        spec: 地形规格，用于取每级的难度值。
+        num_levels: 难度等级数（矩阵行数）。
+    """
+    success = np.asarray(per_env["success"], dtype=np.float64)
+    max_progress = np.asarray(per_env["max_progress"], dtype=np.float64)
+    max_tilt = np.asarray(per_env["max_tilt"], dtype=np.float64)
+    level_ids = np.asarray(level_ids, dtype=np.int64)
+    type_ids = np.asarray(type_ids, dtype=np.int64)
+
+    if not (success.shape == max_progress.shape == max_tilt.shape == level_ids.shape == type_ids.shape):
+        raise ValueError("逐环境数组与网格编号数组长度不一致。")
+
+    per_type: dict[str, Any] = {}
+    for type_index, terrain_type in enumerate(terrain_types):
+        level_values = spec.levels_for(terrain_type, num_levels)
+        rates: list[float] = []
+        level_metrics: list[dict[str, Any]] = []
+        for level_index, level_value in enumerate(level_values):
+            mask = (type_ids == type_index) & (level_ids == level_index)
+            total = int(np.count_nonzero(mask))
+            if total == 0:
+                rates.append(float("nan"))
+                level_metrics.append(
+                    {
+                        "level": level_index + 1,
+                        "difficulty": float(level_value),
+                        "success_rate": float("nan"),
+                        "success_count": 0.0,
+                        "num_envs": 0,
+                        "avg_progress_m": float("nan"),
+                        "avg_max_tilt_rad": float("nan"),
+                    }
+                )
+                continue
+            count = float(np.count_nonzero(success[mask] > 0.5))
+            rates.append(count / total)
+            level_metrics.append(
+                {
+                    "level": level_index + 1,
+                    "difficulty": float(level_value),
+                    "success_rate": count / total,
+                    "success_count": count,
+                    "num_envs": total,
+                    "avg_progress_m": float(np.mean(max_progress[mask])),
+                    "avg_max_tilt_rad": float(np.mean(max_tilt[mask])),
+                }
+            )
+
+        per_type[terrain_type] = {
+            "levels": level_metrics,
+            "success_rate": rates,
+            "success_rate_accumulated": accumulated_success(rates),
+            "score": upper_limit_score(rates),
+            "max_level": len(level_values),
+            "difficulty_unit": spec.unit.get(terrain_type, ""),
+        }
+
+    # 打成便于打印与比较的矩阵：行 = 难度等级，列 = 地形类型
+    success_matrix: list[list[float]] = []
+    accumulated_matrix: list[list[float]] = []
+    difficulty_matrix: list[list[float]] = []
+    for level_index in range(num_levels):
+        success_row: list[float] = []
+        accumulated_row: list[float] = []
+        difficulty_row: list[float] = []
+        for terrain_type in terrain_types:
+            entry = per_type[terrain_type]
+            rates = entry["success_rate"]
+            accumulated = entry["success_rate_accumulated"]
+            levels = entry["levels"]
+            success_row.append(rates[level_index] if level_index < len(rates) else float("nan"))
+            accumulated_row.append(accumulated[level_index] if level_index < len(accumulated) else float("nan"))
+            difficulty_row.append(levels[level_index]["difficulty"] if level_index < len(levels) else float("nan"))
+        success_matrix.append(success_row)
+        accumulated_matrix.append(accumulated_row)
+        difficulty_matrix.append(difficulty_row)
+
+    return {
+        "benchmark": spec.name,
+        "terrain_types": list(terrain_types),
+        "num_levels": num_levels,
+        "difficulty_unit": {terrain_type: spec.unit.get(terrain_type, "") for terrain_type in terrain_types},
+        "per_type": per_type,
+        "scores": {terrain_type: per_type[terrain_type]["score"] for terrain_type in terrain_types},
+        "matrix": {
+            "success_rate": success_matrix,
+            "success_rate_accumulated": accumulated_matrix,
+            "difficulty": difficulty_matrix,
+        },
+    }
diff --git a/eval_kit/core/metrics.py b/eval_kit/core/metrics.py
new file mode 100644
index 0000000..695fa75
--- /dev/null
+++ b/eval_kit/core/metrics.py
@@ -0,0 +1,262 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""指标计算：纯函数，只依赖 numpy。
+
+输入是 :class:`~eval_kit.core.schema.Trace`、:class:`~eval_kit.core.schema.RobotProfile`
+和 :class:`~eval_kit.core.schema.EvalProtocol`，输出是一个扁平的指标字典。
+
+指标口径与 ``scripts/reinforcement_learning/rsl_rl/eval.py`` 保持一致（同样的取点、
+同样的公式），因此默认协议下两套评估的数值可以直接对照；差别只在三处，且都是
+有意为之：
+
+1. **质量**不再写死在命令行默认值里，而是从 ``Trace.mass_kg`` 取（适配层从资产读），
+   传入的 ``profile.mass_kg`` 只作兜底。
+2. **加速度阈值**以重力倍数表达，换算成 m/s² 后参与计算，使阈值可跨量级机器人复用。
+3. **缺失的形态**返回 ``nan`` 而不是 0。旧实现里没有足端的机器人会得到
+   「支撑裕度 = 0.00」，看起来像测出来的值，实际是没测。
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from eval_kit.core.schema import EvalProtocol, RobotProfile, Trace
+
+#: 全部帧，用于成功判定与最大进度（含起始帧，与旧实现取点一致）。
+_ALL_FRAMES = slice(None)
+#: 动作执行之后的帧，用于逐帧累积类指标。
+_STEP_FRAMES = slice(1, None)
+
+_GRAVITY = 9.81
+
+
+def tilt_from_projected_gravity(projected_gravity_b: np.ndarray) -> np.ndarray:
+    """由机体系投影重力算倾角 [rad]。
+
+    ``projected_gravity_b`` 为 ``(T, N, 3)``，返回 ``(T, N)``。
+    """
+    z_axis = np.clip(-projected_gravity_b[..., 2], -1.0, 1.0)
+    return np.arccos(z_axis)
+
+
+def support_margin_xy(root_xy: np.ndarray, foot_xy: np.ndarray) -> np.ndarray:
+    """机身投影点到足端轴对齐包围盒的距离 [m]，在盒内为正、盒外为负。
+
+    ``root_xy`` 为 ``(N, 2)``，``foot_xy`` 为 ``(N, F, 2)``，返回 ``(N,)``。
+
+    这是当前实现采用的口径（与旧 ``eval.py`` 相同），记录在
+    ``EvalProtocol.stability_margin_method`` 里。它比凸包/ZMP 版本粗糙，
+    但不会在退化多边形上失败，也不需要 scipy。
+    """
+    min_xy = np.min(foot_xy, axis=1)
+    max_xy = np.max(foot_xy, axis=1)
+
+    inside_x = (root_xy[:, 0] >= min_xy[:, 0]) & (root_xy[:, 0] <= max_xy[:, 0])
+    inside_y = (root_xy[:, 1] >= min_xy[:, 1]) & (root_xy[:, 1] <= max_xy[:, 1])
+    inside = inside_x & inside_y
+
+    inside_dist = np.minimum(
+        np.minimum(root_xy[:, 0] - min_xy[:, 0], max_xy[:, 0] - root_xy[:, 0]),
+        np.minimum(root_xy[:, 1] - min_xy[:, 1], max_xy[:, 1] - root_xy[:, 1]),
+    )
+    outside_dx = np.clip(np.maximum(min_xy[:, 0] - root_xy[:, 0], root_xy[:, 0] - max_xy[:, 0]), 0.0, None)
+    outside_dy = np.clip(np.maximum(min_xy[:, 1] - root_xy[:, 1], root_xy[:, 1] - max_xy[:, 1]), 0.0, None)
+    outside_dist = np.sqrt(outside_dx**2 + outside_dy**2)
+
+    return np.where(inside, inside_dist, -outside_dist)
+
+
+def _nanmean_or_nan(values: np.ndarray) -> float:
+    """均值；空数组返回 nan（而不是 0，也不是 numpy 的警告）。"""
+    if values.size == 0:
+        return float("nan")
+    return float(np.mean(values))
+
+
+def compute_metrics(
+    trace: Trace,
+    profile: RobotProfile,
+    protocol: EvalProtocol,
+    *,
+    include_per_env: bool = False,
+) -> dict[str, Any]:
+    """由一次 rollout 的轨迹算出全部指标。
+
+    指标名的命名与旧 ``eval.py`` 完全一致，便于新旧结果直接对照。
+    """
+    num_steps = trace.num_steps
+    num_envs = trace.num_envs
+    dt = float(trace.dt)
+
+    if abs(protocol.dt - dt) > 0.01 * dt:
+        raise ValueError(
+            f"协议 dt={protocol.dt} 与轨迹 dt={dt} 不一致。"
+            "通常意味着协议里的步长与任务的 decimation 不匹配。"
+        )
+
+    command = np.asarray(trace.command, dtype=np.float64)
+    command_xy = command[:2]
+    command_yaw = float(command[2])
+
+    command_dir = command_xy.copy()
+    norm = float(np.linalg.norm(command_dir))
+    command_dir = command_dir / norm if norm > 1e-6 else np.array([1.0, 0.0])
+
+    # ------------------------------------------------------------------ 成功判定
+    base_xy = np.asarray(trace.base_pos_w, dtype=np.float64)[..., :2]
+    delta_xy = base_xy - base_xy[0][None, :, :]
+    progress = np.einsum("tnc,c->tn", delta_xy[_ALL_FRAMES], command_dir)
+    max_progress = progress.max(axis=0)
+    reached = max_progress >= protocol.success_distance_m
+
+    tilt = tilt_from_projected_gravity(np.asarray(trace.projected_gravity_b, dtype=np.float64))
+    tilt_steps = tilt[_STEP_FRAMES]
+    max_tilt = tilt_steps.max(axis=0)
+    ever_fallen = max_tilt > protocol.tilt_threshold_rad
+
+    success = reached & ~ever_fallen
+    success_count = int(np.count_nonzero(success))
+
+    # 首次达成成功的时刻：第 i 帧（i>=1）达成则记 i*dt。
+    crossed = progress[_STEP_FRAMES] >= protocol.success_distance_m
+    complete_time = np.full((num_envs,), np.nan, dtype=np.float64)
+    for env_index in range(num_envs):
+        hit = np.flatnonzero(crossed[:, env_index])
+        if hit.size:
+            complete_time[env_index] = float(hit[0] + 1) * dt
+
+    successful_time = complete_time[success & np.isfinite(complete_time)]
+    avg_time = _nanmean_or_nan(successful_time)
+
+    # ------------------------------------------------------------- 速度跟踪误差
+    lin_vel_b = np.asarray(trace.base_lin_vel_b, dtype=np.float64)[_STEP_FRAMES]
+    ang_vel_b = np.asarray(trace.base_ang_vel_b, dtype=np.float64)[_STEP_FRAMES]
+    lin_error = np.linalg.norm(lin_vel_b[..., :2] - command_xy[None, None, :], axis=-1)
+    yaw_error = np.abs(ang_vel_b[..., 2] - command_yaw)
+
+    # -------------------------------------------------------------- 能耗与路程
+    joint_torque = np.asarray(trace.joint_torque, dtype=np.float64)[_STEP_FRAMES]
+    joint_vel = np.asarray(trace.joint_vel, dtype=np.float64)[_STEP_FRAMES]
+    mechanical_power = joint_torque * joint_vel
+    if protocol.energy_convention == "absolute":
+        integrand = np.abs(mechanical_power)
+    elif protocol.energy_convention == "signed":
+        integrand = mechanical_power
+    else:
+        raise ValueError(
+            f"未知能耗约定 {protocol.energy_convention!r}；可选 'absolute' 或 'signed'。"
+        )
+    energy = integrand.sum(axis=(0, 2)) * dt
+
+    lin_vel_w = np.asarray(trace.base_lin_vel_w, dtype=np.float64)
+    route = np.linalg.norm(lin_vel_w[_STEP_FRAMES][..., :2], axis=-1).sum(axis=0) * dt
+
+    # ------------------------------------------------------------ 运动不稳定性
+    accel = np.linalg.norm(np.diff(lin_vel_w, axis=0), axis=-1) / dt
+    instability_count = (accel > protocol.acc_threshold_mps2).sum(axis=0)
+
+    stable_time = (tilt_steps < protocol.tilt_threshold_rad).sum(axis=0) * dt
+
+    # ------------------------------------------------------------- 支撑多边形
+    feet_pos_w = np.asarray(trace.feet_pos_w, dtype=np.float64)[_STEP_FRAMES]
+    num_feet = int(feet_pos_w.shape[2])
+    if num_feet > 0:
+        margins = support_margin_xy(base_xy[_STEP_FRAMES][:, :, :].reshape(-1, 2), feet_pos_w.reshape(-1, num_feet, 2))
+        margins = margins.reshape(feet_pos_w.shape[0], num_envs)
+        avg_margin = _nanmean_or_nan(margins)
+        margin_method = protocol.stability_margin_method
+    else:
+        avg_margin = float("nan")
+        margin_method = "unavailable"
+
+    # --------------------------------------------------------------- 支撑接触
+    support_force = np.asarray(trace.support_force_n, dtype=np.float64)
+    support_contact_time = np.asarray(trace.support_contact_time_s, dtype=np.float64)
+    num_support_bodies = int(support_force.shape[2])
+    support_threshold = (
+        profile.support_force_threshold_n
+        if protocol.support_force_threshold_n is None
+        else protocol.support_force_threshold_n
+    )
+    if num_support_bodies > 0:
+        active = np.any(support_force[_STEP_FRAMES] > support_threshold, axis=2)  # (T-1, N)
+        support_steps = active.sum(axis=0)
+        # 起始帧之前视为「未接触」，因此首帧接触也算一次事件（与旧实现一致）。
+        active_padded = np.concatenate([np.zeros((1, num_envs), dtype=bool), active], axis=0)
+        support_events = (active_padded[1:] & ~active_padded[:-1]).sum(axis=0)
+        max_support_time = support_contact_time[_STEP_FRAMES].max(axis=(0, 2))
+        support_contact_ratio = _nanmean_or_nan(support_steps / max(num_steps, 1))
+        avg_support_events = float(np.mean(support_events))
+        avg_max_support_time = float(np.mean(max_support_time))
+        if success_count > 0:
+            success_support_ratio = float(np.mean((support_events[success] > 0).astype(np.float64)))
+        else:
+            success_support_ratio = float("nan")
+    else:
+        support_contact_ratio = float("nan")
+        avg_support_events = float("nan")
+        avg_max_support_time = float("nan")
+        success_support_ratio = float("nan")
+
+    # --------------------------------------------------------- 质量与 COT
+    mass = trace.mass_kg if trace.mass_kg is not None else profile.mass_kg
+    if mass is None or mass <= 0.0:
+        cost_of_transport = np.full((num_envs,), np.nan)
+        mass_used = float("nan")
+    else:
+        cost_of_transport = energy / ((route + 1e-6) * mass * _GRAVITY)
+        mass_used = float(mass)
+
+    metrics: dict[str, Any] = {
+        "profile": profile.name,
+        "protocol_fingerprint": protocol.fingerprint(),
+        "num_envs": float(num_envs),
+        "num_steps": float(num_steps),
+        "episode_length_s": float(num_steps * dt),
+        "terrain_size_m": float(protocol.terrain_size_m),
+        "success_distance_m": float(protocol.success_distance_m),
+        "robot_mass_kg": mass_used,
+        "energy_convention": protocol.energy_convention,
+        "stability_margin_method": margin_method,
+        "tilt_threshold_rad": float(protocol.tilt_threshold_rad),
+        "acc_threshold_mps2": float(protocol.acc_threshold_mps2),
+        "support_force_threshold_n": float(support_threshold) if num_support_bodies > 0 else float("nan"),
+        # --- 成功率与进度
+        "success_count": float(success_count),
+        "success_rate": float(success_count / max(num_envs, 1)),
+        "avg_final_progress_m": float(np.mean(max_progress)),
+        # --- 时间
+        "avg_time_consumption_s": avg_time,
+        "time_efficiency": float(
+            avg_time / (protocol.success_distance_m / max(abs(float(command[0])), 1e-6))
+        )
+        if np.isfinite(avg_time)
+        else float("nan"),
+        # --- 跟踪误差
+        "mean_lin_vel_error": float(np.mean(lin_error)),
+        "mean_yaw_vel_error": float(np.mean(yaw_error)),
+        # --- 能耗
+        "avg_energy_j": float(np.mean(energy)),
+        "avg_cost_of_transport": _nanmean_or_nan(cost_of_transport),
+        # --- 稳定性
+        "unstability_rate": float(np.mean(instability_count / max(num_steps, 1))),
+        "avg_stability_margin_m": avg_margin,
+        "avg_max_tilt_rad": float(np.mean(max_tilt)),
+        "avg_stable_time_s": float(np.mean(stable_time)),
+        # --- 支撑接触
+        "avg_support_contact_ratio": support_contact_ratio,
+        "avg_support_events": avg_support_events,
+        "avg_max_support_contact_time_s": avg_max_support_time,
+        "successful_runs_with_support_ratio": success_support_ratio,
+    }
+
+    if include_per_env:
+        metrics["_per_env"] = {
+            "success": success.astype(float).tolist(),
+            "max_progress": max_progress.tolist(),
+            "max_tilt": max_tilt.tolist(),
+        }
+    return metrics
diff --git a/eval_kit/core/naming.py b/eval_kit/core/naming.py
new file mode 100644
index 0000000..07eac9b
--- /dev/null
+++ b/eval_kit/core/naming.py
@@ -0,0 +1,94 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""按正则列表解析名字，与 IsaacLab 的 ``resolve_matching_names`` 语义一致，
+但**匹配不到不抛异常**。
+
+IsaacLab 的实现用 ``re.fullmatch`` 逐个比对，并且：某个名字同时命中多个正则会报错，
+某个正则一个都没命中也报错。对环境配置来说这种严格是有用的（写错了应该立刻发现），
+但评价代码不该因为「这个机器人没有轮子」就崩掉——那时正确行为是跳过依赖轮子的指标。
+
+这个模块是纯逻辑，不依赖 isaaclab，因此可以单独测试。
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Sequence
+
+
+class NameResolution:
+    """一次名字解析的结果。"""
+
+    __slots__ = ("indices", "names", "unmatched_patterns", "ambiguous_names")
+
+    def __init__(
+        self,
+        indices: list[int],
+        names: list[str],
+        unmatched_patterns: list[str],
+        ambiguous_names: list[tuple[str, list[str]]],
+    ) -> None:
+        self.indices = indices
+        self.names = names
+        #: 一个都没命中的正则。
+        self.unmatched_patterns = unmatched_patterns
+        #: 同时命中多个正则的名字，以及命中的正则。
+        self.ambiguous_names = ambiguous_names
+
+    def __bool__(self) -> bool:
+        return bool(self.indices)
+
+    def __len__(self) -> int:
+        return len(self.indices)
+
+    def __repr__(self) -> str:
+        return (
+            f"NameResolution({len(self.indices)} 项, "
+            f"未命中={self.unmatched_patterns}, 多重命中={self.ambiguous_names})"
+        )
+
+
+def resolve_matching_indices(patterns: Sequence[str], names: Sequence[str]) -> NameResolution:
+    """把正则列表解析成 ``names`` 中的下标。
+
+    行为与 IsaacLab 的 ``resolve_matching_names`` 一致之处：用 ``re.fullmatch``；
+    返回顺序遵循 ``patterns`` 的顺序。
+    不同之处：不抛异常，而是把「未命中」和「多重命中」记在返回值里，由调用方决定
+    是警告还是忽略。
+
+    Args:
+        patterns: 正则列表，例如 ``("base_link", ".*_knee")``。
+        names: 候选名字，例如 ``articulation.body_names``。
+    """
+    hits: dict[int, list[str]] = {}
+    pattern_hits: dict[str, int] = {pattern: 0 for pattern in patterns}
+
+    for index, name in enumerate(names):
+        for pattern in patterns:
+            try:
+                matched = re.fullmatch(pattern, name) is not None
+            except re.error as exc:  # 正则本身写错，属于配置错误，应当立刻暴露
+                raise ValueError(f"机器人描述里的正则 {pattern!r} 无法编译：{exc}") from exc
+            if matched:
+                hits.setdefault(index, []).append(pattern)
+                pattern_hits[pattern] += 1
+
+    indices: list[int] = []
+    resolved_names: list[str] = []
+    ambiguous: list[tuple[str, list[str]]] = []
+    taken: set[int] = set()
+
+    # 按 patterns 顺序输出，跳过已被更早的正则取走的下标
+    for pattern in patterns:
+        for index, matched_patterns in hits.items():
+            if pattern not in matched_patterns or index in taken:
+                continue
+            taken.add(index)
+            indices.append(index)
+            resolved_names.append(names[index])
+            if len(matched_patterns) > 1:
+                ambiguous.append((names[index], list(matched_patterns)))
+
+    unmatched = [pattern for pattern, count in pattern_hits.items() if count == 0]
+    return NameResolution(indices, resolved_names, unmatched, ambiguous)
diff --git a/eval_kit/core/report.py b/eval_kit/core/report.py
new file mode 100644
index 0000000..c5e98fa
--- /dev/null
+++ b/eval_kit/core/report.py
@@ -0,0 +1,191 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""结果的格式化与筛选：纯函数，只依赖 numpy 与标准库。
+
+输出格式与旧 ``eval.py`` 保持一致，便于对照。``filter_metrics`` 决定每种
+``eval_type`` 关心哪些指标。
+"""
+
+from __future__ import annotations
+
+from typing import Any, Sequence
+
+import numpy as np
+
+#: 支持的评估类型。
+EVAL_TYPES: tuple[str, ...] = (
+    "all",
+    "upper_limits",
+    "velocity_error",
+    "access_rate",
+    "time_consumption",
+    "energy_consumption",
+    "motion_stability",
+    "stability_margin",
+    "robustness",
+)
+
+#: 每种评估类型关心哪些指标。
+METRIC_SELECTION: dict[str, tuple[str, ...]] = {
+    "velocity_error": ("mean_lin_vel_error", "mean_yaw_vel_error"),
+    "access_rate": ("success_count", "success_rate", "avg_final_progress_m"),
+    "time_consumption": ("success_count", "avg_time_consumption_s", "time_efficiency"),
+    "energy_consumption": ("avg_energy_j", "avg_cost_of_transport"),
+    "motion_stability": ("unstability_rate",),
+    "stability_margin": ("avg_stability_margin_m",),
+    "robustness": ("success_rate", "avg_max_tilt_rad", "avg_stable_time_s"),
+}
+
+
+def filter_metrics(metrics: dict[str, Any], eval_type: str) -> dict[str, Any]:
+    """按评估类型裁剪指标字典；``all`` 返回全部。"""
+    if eval_type == "all":
+        return metrics
+    if eval_type not in METRIC_SELECTION:
+        raise KeyError(f"未知评估类型 {eval_type!r}；可选：{sorted(EVAL_TYPES)}")
+    return {key: metrics[key] for key in METRIC_SELECTION[eval_type] if key in metrics}
+
+
+def _format_cell(value: float, width: int = 14) -> str:
+    if value is None or not np.isfinite(value):
+        return "   -".rjust(width)
+    return f"{value:.2f}".rjust(width)
+
+
+def format_matrix(matrix: Sequence[Sequence[float]], terrain_types: Sequence[str], *, title: str) -> str:
+    """把「行=等级、列=地形」的矩阵格式化成字符串。"""
+    width = 14
+    header = "level".ljust(8) + "".join(terrain_type[: width - 1].rjust(width) for terrain_type in terrain_types)
+    lines = [title, header]
+    for level_index, row in enumerate(matrix, start=1):
+        cells = "".join(_format_cell(float(value), width) for value in row)
+        lines.append(f"{level_index:<8}{cells}")
+    return "\n".join(lines)
+
+
+def format_numpy_matrix(matrix: Sequence[Sequence[float]], *, title: str) -> str:
+    """紧凑的 numpy 风格矩阵，便于贴进报告；nan 显示成 ``-``。
+
+    用 ``formatter`` 而不是 ``nanstr``——后者是 ``np.set_printoptions`` 的参数，
+    不是 ``np.array2string`` 的关键字（numpy 1.21 与 1.26 都会对前者报 TypeError）。
+    """
+    array = np.round(np.array(matrix, dtype=np.float32), 2)
+
+    def _cell(value: float) -> str:
+        return "   -" if not np.isfinite(value) else f"{value:.2f}"
+
+    body = np.array2string(array, formatter={"float_kind": _cell})
+    return f"{title}:\n{body}"
+
+
+def format_upper_limit_summary(result: dict[str, Any]) -> str:
+    """上限评估的汇总输出：成功率矩阵、累积成功率矩阵、各类型 score。"""
+    terrain_types = result["terrain_types"]
+    matrix = result["matrix"]
+    scores = np.array([result["scores"][terrain_type] for terrain_type in terrain_types], dtype=np.float32)
+    lines = [
+        format_numpy_matrix(matrix["success_rate"], title="success rate"),
+        format_numpy_matrix(matrix["success_rate_accumulated"], title="success rate accumulated"),
+        f"score: {np.array2string(scores, precision=2, suppress_small=True)} of {result['num_levels']}",
+    ]
+    units = {unit for unit in result.get("difficulty_unit", {}).values() if unit}
+    if units:
+        lines.append(f"difficulty units: {'; '.join(sorted(units))}")
+    return "\n".join(lines)
+
+
+def format_rollout_summary(metrics: dict[str, Any]) -> str:
+    """混合地形汇总跑的全指标输出。"""
+
+    def fmt(key: str, spec: str = "{:.2f}") -> str:
+        value = float(metrics.get(key, float("nan")))
+        return "nan" if not np.isfinite(value) else spec.format(value)
+
+    success_ratio = float(metrics.get("successful_runs_with_support_ratio", float("nan")))
+    success_ratio_text = "nan" if not np.isfinite(success_ratio) else f"{success_ratio * 100:.1f}%"
+    support_ratio = float(metrics.get("avg_support_contact_ratio", float("nan")))
+    support_ratio_text = "nan" if not np.isfinite(support_ratio) else f"{support_ratio * 100:.1f}%"
+    mass = float(metrics.get("robot_mass_kg", float("nan")))
+    mass_text = "nan" if not np.isfinite(mass) else f"{mass:.3f}"
+
+    return "\n".join(
+        [
+            f"success rate: {fmt('success_rate')}",
+            f"success count: {int(round(float(metrics.get('success_count', 0.0))))} "
+            f"time consumption: {fmt('avg_time_consumption_s')}",
+            f"time efficiency: {fmt('time_efficiency')}",
+            f"mean lin vel error: {fmt('mean_lin_vel_error')} "
+            f"mean yaw vel error: {fmt('mean_yaw_vel_error')}",
+            f"average energy: {fmt('avg_energy_j')} J "
+            f"average cost of transport: {fmt('avg_cost_of_transport')}",
+            f"unstability rate: {fmt('unstability_rate', '{:.3f}')} "
+            f"average stability margin: {fmt('avg_stability_margin_m')}",
+            f"average max tilt: {fmt('avg_max_tilt_rad')} rad "
+            f"average stable time: {fmt('avg_stable_time_s')} s",
+            f"support contact ratio: {support_ratio_text} "
+            f"events/episode: {fmt('avg_support_events')} "
+            f"max duration: {fmt('avg_max_support_contact_time_s')} s "
+            f"successful runs using support: {success_ratio_text}",
+            f"[protocol] mass={mass_text} kg energy={metrics.get('energy_convention')} "
+            f"margin={metrics.get('stability_margin_method')} "
+            f"tilt>={fmt('tilt_threshold_rad')} acc>={fmt('acc_threshold_mps2')} "
+            f"fingerprint={metrics.get('protocol_fingerprint')}",
+        ]
+    )
+
+
+def format_aggregate_summary(aggregate: dict[str, Any], keys: Sequence[str] | None = None) -> str:
+    """多次试验的 ``mean ± std`` 汇总。
+
+    做机器人对比时应看这个而不是单次数值。
+    """
+    summary = aggregate.get("summary", {})
+    selected = list(keys) if keys is not None else list(summary.keys())
+    lines = [f"runs = {aggregate.get('num_runs')}  seeds = {aggregate.get('seeds')}"]
+    for key in selected:
+        entry = summary.get(key)
+        if entry is None:
+            continue
+        mean = float(entry["mean"])
+        std = float(entry["std"])
+        if not np.isfinite(mean):
+            lines.append(f"  {key:<38} nan（{entry.get('finite_runs', 0)}/{aggregate.get('num_runs')} 次有效）")
+        else:
+            lines.append(f"  {key:<38} {mean:>10.4f} ± {std:<8.4f} [{entry['min']:.4f}, {entry['max']:.4f}]")
+    return "\n".join(lines)
+
+
+def format_metric_result(eval_type: str, metrics: dict[str, Any]) -> str:
+    """单一评估类型的精简输出。"""
+
+    def fmt(key: str) -> str:
+        value = float(metrics.get(key, float("nan")))
+        return "nan" if not np.isfinite(value) else f"{value:.2f}"
+
+    if eval_type == "velocity_error":
+        return f"mean lin vel error: {fmt('mean_lin_vel_error')}\nmean yaw vel error: {fmt('mean_yaw_vel_error')}"
+    if eval_type == "access_rate":
+        return f"success rate: {fmt('success_rate')}\navg final progress: {fmt('avg_final_progress_m')} m"
+    if eval_type == "time_consumption":
+        return (
+            f"success count: {int(round(float(metrics.get('success_count', 0.0))))} "
+            f"time consumption: {fmt('avg_time_consumption_s')}\n"
+            f"time efficiency: {fmt('time_efficiency')}"
+        )
+    if eval_type == "energy_consumption":
+        return f"average energy: {fmt('avg_energy_j')} J\naverage cost of transport: {fmt('avg_cost_of_transport')}"
+    if eval_type == "motion_stability":
+        return f"unstability rate: {fmt('unstability_rate')}"
+    if eval_type == "stability_margin":
+        return (
+            f"average stability margin: {fmt('avg_stability_margin_m')} m "
+            f"(method: {metrics.get('stability_margin_method')})"
+        )
+    if eval_type == "robustness":
+        return (
+            f"success rate: {fmt('success_rate')}\n"
+            f"average max tilt: {fmt('avg_max_tilt_rad')} rad\n"
+            f"average stable time: {fmt('avg_stable_time_s')} s"
+        )
+    return format_rollout_summary(metrics)
diff --git a/eval_kit/core/schema.py b/eval_kit/core/schema.py
new file mode 100644
index 0000000..9afbda4
--- /dev/null
+++ b/eval_kit/core/schema.py
@@ -0,0 +1,392 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""评价套件的数据契约。
+
+三个对象构成整条流水线：
+
+``RobotProfile``   机器人形态（谁是足端、谁是支撑体、质量多少）
+``EvalProtocol``   评估协议（指令、时长、各种阈值、能耗约定）
+``Trace``          一次 rollout 的标准化逐帧记录
+
+上游（任何框架的适配器）负责产出 ``Trace``，下游（:mod:`eval_kit.core.metrics`）
+只从 ``Trace`` 计算指标。指标代码因此不接触任何仿真对象。
+
+这个模块只依赖 numpy 与标准库。
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import asdict, dataclass
+from typing import Any
+
+import numpy as np
+
+#: 帧索引约定：``Trace`` 的第 0 帧是执行动作之前的起始状态，
+#: 第 ``i`` 帧（``i >= 1``）是第 ``i`` 步动作之后的状态。
+#: 因此一次 ``steps`` 步的 rollout 会产生 ``steps + 1`` 帧。
+START_FRAME = 0
+
+#: 成功判定与最大进度使用全部帧（含起始帧），与 legged_gym 侧
+#: ``criterion.py`` 及现有 ``eval.py`` 的取点一致。
+PROGRESS_FRAMES = slice(None)
+
+#: 逐帧累积类指标只使用动作之后的帧。
+STEP_FRAMES = slice(1, None)
+
+
+@dataclass(frozen=True)
+class RobotProfile:
+    """机器人形态描述。
+
+    评价代码里不应出现具体的 link/joint 名字或质量常数，全部集中在这里。
+    新增一个机器人 = 新增一条 ``RobotProfile`` 记录，不改评价代码。
+    """
+
+    name: str
+    """机器人标识，同时也是输出文件名的一部分。"""
+
+    mass_kg: float | None
+    """整机质量 [kg]；``None`` 表示运行时从仿真资产读取（推荐）。"""
+
+    feet_body_patterns: tuple[str, ...] = ()
+    """支撑多边形的接触点，例如火蚁的四个轮。空元组表示该机器人不计算支撑裕度。"""
+
+    support_body_patterns: tuple[str, ...] = ()
+    """允许作为「短暂支撑」的机体，例如火蚁的机身与膝部。空元组表示不做支撑统计。"""
+
+    actuated_joint_patterns: tuple[str, ...] = ()
+    """参与能耗统计的关节。"""
+
+    support_force_threshold_n: float = 15.0
+    """判定「支撑接触」的法向力阈值 [N]。"""
+
+    body_length_m: float = 0.0
+    """机身长度 [m]，仅用于校验地形瓦片尺寸是否够大。"""
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass(frozen=True)
+class EvalProtocol:
+    """评估协议：一次评估的全部「口径」。
+
+    这些字段决定数值大小，因此都会被写进输出 JSON 的 ``fingerprint``，
+    避免不同设置下的结果被悄悄拿来比较。
+
+    默认值与现有 ``scripts/reinforcement_learning/rsl_rl/eval.py`` 的默认
+    命令行参数一致（能耗用绝对值口径、倾角阈值 0.70 rad、加速度阈值 2.0 m/s²
+    折算成重力倍数），因此默认协议下的新结果可以直接和旧评估结果对照。
+    """
+
+    steps: int = 500
+    """每个 episode 的控制步数。dt=0.02 时 500 步 = 10 s。"""
+
+    dt: float = 0.02
+    """控制步长 [s]，即 ``decimation * sim.dt``。"""
+
+    command: tuple[float, float, float] = (1.0, 0.0, 0.0)
+    """固定速度指令 ``(lin_vel_x, lin_vel_y, ang_vel_z)``。"""
+
+    start_pose: tuple[float, float, float] = (-0.9, 0.0, 0.0)
+    """起始位姿 ``(x, y, yaw)``，相对地形瓦片中心。"""
+
+    start_z_offset_m: float = 0.0
+    """起始高度相对地形瓦片中心高度的额外抬升 [m]。
+
+    默认 0.0 表示把机器人直接放在 ``env_origin.z`` 上，与旧 ``eval.py`` 的写法一致。
+    但那样只对「瓦片中心是最高点」的地形成立：像倒金字塔台阶（``pyramid_stairs_inv``，
+    中心是最低点）这类地形，机器人在 ``|x| > platform`` 处会被直接生成到地形几何体
+    内部，卡住无法移动，导致该地形在所有难度上都得到 0 成功率。
+
+    给一个正值（例如 0.3 m）让机器人从地形表面上方落下，可以适配任意地形形状。
+    改这个值会改变 ``protocol_fingerprint``。
+    """
+
+    success_distance_m: float = 2.0
+    """沿指令方向前进多少米算成功。"""
+
+    tilt_threshold_rad: float = 0.70
+    """倾角超过该值 [rad] 视为摔倒。0.70 rad ≈ 40°。"""
+
+    acc_threshold_g: float = 2.0 / 9.81
+    """机身加速度超过该值（单位 g）计为一次不稳定。用重力倍数而非绝对 m/s²，
+    使该阈值对不同量级的机器人保持可比。默认值 2.0 m/s² 折算而来。"""
+
+    energy_convention: str = "absolute"
+    """能耗积分约定，``"absolute"`` 表示 ``Σ|τ·q̇|·dt``，
+    ``"signed"`` 表示 ``Σ(τ·q̇)·dt``。两者数值差异可达一倍以上，
+    全局必须一致；该值会写进输出 JSON。"""
+
+    stability_margin_method: str = "foot_bbox"
+    """支撑裕度算法。``"foot_bbox"`` = 机体到足端轴对齐包围盒的距离（当前实现）。"""
+
+    support_force_threshold_n: float | None = 15.0
+    """判定支撑接触的法向力阈值 [N]；``None`` 时改用机器人描述里的值。"""
+
+    enable_interval_pushes: bool = False
+    """是否启用周期性外力扰动（``robustness`` 评估用）。"""
+
+    enforce_terrain_bounds: bool = True
+    """是否保留 IsaacLab 的 ``terrain_out_of_bounds`` 终止项。
+
+    **默认 True 是为了与旧 ``eval.py`` 的结果可比，但已知它有系统性偏差**：
+    该终止项比较的是机器人的世界坐标与整张地图的范围，而不是它自己那块地砖。
+    在地砖 2 m、网格 5x5 或 8xN 的评估配置下，最外侧的格子会被误判为越界，
+    机器人从第 0 步起每步都被重置，永远没机会尝试地形；而 ``|y|`` 恰好等于阈值
+    的那两列则处于浮点边界上。实测：8x2 网格下首步就有 6/32 个环境被误判。
+
+    三种处置方式（建议前两种，按顺序）：
+
+    1. 把 :attr:`terrain_size_m` 提到 **≥ 3.8**，即可让所有起始位置落进阈值内，
+       同时保留这个终止项；
+    2. 置为 ``False``，评估时去掉这个终止项（成功判定本来就是按前进距离算的，
+       走出地砖不构成失败）；
+    3. 保持 ``True`` 且用小地砖 —— 数值会偏低，且被影响的难度等级因行序打乱而
+       每次不同，不建议用来做对比。
+    """
+
+    push_interval_s: tuple[float, float] = (2.0, 2.01)
+    """扰动间隔 [s]，取一个极窄区间让扰动近似等周期但可比较。"""
+
+    push_velocity_range: tuple[float, float] = (-0.5, 0.5)
+    """每个水平方向上的扰动速度范围 [m/s]。"""
+
+    terrain_spec: str = "standard_v1"
+    """地形等级规格名，见 :mod:`eval_kit.core.terrain_spec`。"""
+
+    terrain_size_m: float = 2.0
+    """单块地形瓦片边长 [m]。"""
+
+    num_levels: int = 8
+    """上限评估的难度等级数（网格行数）。"""
+
+    envs_per_cell: int = 1
+    """上限评估每个「地形类型 × 难度」格子里放几个机器人。
+
+    默认 1。**注意：由于起始位姿被钉死，同一格的多个机器人起始坐标完全相同**，
+    它们不是独立样本，差异只来自物理求解器的非确定性。所以靠「一格放多个」来提高
+    样本量是假精度；正确做法是 ``envs_per_cell=1`` + 多个 :attr:`seeds`
+    （不同种子会生成不同的地形实例，因此跨种子才是独立样本）。
+
+    代价：``envs_per_cell=1`` 时单次评估里每格成功率只能是 0 或 1，
+    必须配合足够多的种子才能得到有意义的分辨率。经验值：种子数 ≥ 8 时
+    每格分辨率达到 1/8 ≈ 12.5%。
+    """
+
+    seeds: tuple[int, ...] = (0,)
+    """重复试验的随机种子。单次 rollout 噪声很大，比较机器人时应跑多个种子。
+
+    每个种子会重新生成地形实例（``terrain_generator.seed = seed``），所以跨种子
+    的样本是独立的；这是提高统计可靠性的正确途径，而不是加大 ``envs_per_cell``。
+    """
+
+    @property
+    def acc_threshold_mps2(self) -> float:
+        """加速度阈值的 m/s² 形式，供指标计算使用。"""
+        return self.acc_threshold_g * 9.81
+
+    def minimum_terrain_size_for_bounds(self, *, border_width: float = 2.0, distance_buffer: float = 3.0) -> float:
+        """保留 ``terrain_out_of_bounds`` 终止项且不误判所需的最小地砖边长 [m]。
+
+        这个终止项判定 ``|origin + start| <= 0.5 * 地图边长 - distance_buffer``，
+        而最外侧格子中心在 ``(rows-1)/2 * tile``，地图边长 ``= rows * tile + 2 * border``。
+        两边的 ``rows`` 项相消，于是结论与网格行数无关：
+
+            ``tile >= 2 * (|start_x| + distance_buffer - border_width)``
+
+        默认的 ``border_width=2.0`` / ``distance_buffer=3.0`` 与
+        :func:`eval_kit.isaaclab.env_setup.apply_eval_terrain_baseline` 及 IsaacLab 的
+        默认值一致；取 ``start_x = -0.9`` 得 ``tile >= 3.8 m``。
+
+        放在内核里（而不是适配层）是为了让编排进程也能用——它不启动仿真，
+        无法 import 任何依赖 isaaclab 的模块。
+        """
+        return max(0.0, 2.0 * (abs(self.start_pose[0]) + distance_buffer - border_width))
+
+    def fingerprint(self) -> str:
+        """协议指纹：决定数值的字段的稳定哈希，写进输出 JSON。"""
+        payload = {
+            "steps": self.steps,
+            "dt": self.dt,
+            "command": list(self.command),
+            "start_pose": list(self.start_pose),
+            "start_z_offset_m": self.start_z_offset_m,
+            "success_distance_m": self.success_distance_m,
+            "tilt_threshold_rad": self.tilt_threshold_rad,
+            "acc_threshold_g": self.acc_threshold_g,
+            "energy_convention": self.energy_convention,
+            "stability_margin_method": self.stability_margin_method,
+            "support_force_threshold_n": self.support_force_threshold_n,
+            "enable_interval_pushes": self.enable_interval_pushes,
+            "enforce_terrain_bounds": self.enforce_terrain_bounds,
+            "push_interval_s": list(self.push_interval_s),
+            "push_velocity_range": list(self.push_velocity_range),
+            "terrain_spec": self.terrain_spec,
+            "terrain_size_m": self.terrain_size_m,
+            "num_levels": self.num_levels,
+            "envs_per_cell": self.envs_per_cell,
+        }
+        blob = json.dumps(payload, sort_keys=True).encode("utf-8")
+        return hashlib.sha256(blob).hexdigest()[:12]
+
+    def to_dict(self) -> dict[str, Any]:
+        data = asdict(self)
+        data["acc_threshold_mps2"] = self.acc_threshold_mps2
+        data["fingerprint"] = self.fingerprint()
+        return data
+
+
+@dataclass
+class Trace:
+    """一次 rollout 的标准化逐帧记录。
+
+    所有数组的第一维都是帧、第二维都是环境。第 0 帧是动作执行之前的起始状态。
+
+    ``feet_pos_w`` / ``support_force_n`` / ``support_contact_time_s`` 的第二维
+    可能是 0（该机器人没有足端或没有支撑体语义），指标层会跳过对应指标。
+    """
+
+    dt: float
+    command: np.ndarray
+    """``(3,)`` 本次 rollout 使用的固定指令。"""
+
+    base_pos_w: np.ndarray
+    """``(T, N, 3)`` 机身世界位置。"""
+
+    base_lin_vel_w: np.ndarray
+    """``(T, N, 3)`` 机身世界系线速度，用于路程与加速度（不稳定性）。"""
+
+    base_lin_vel_b: np.ndarray
+    """``(T, N, 3)`` 机体系线速度，用于速度跟踪误差。"""
+
+    base_ang_vel_b: np.ndarray
+    """``(T, N, 3)`` 机体系角速度，用第 2 个分量做 yaw 跟踪误差。"""
+
+    projected_gravity_b: np.ndarray
+    """``(T, N, 3)`` 机体系投影重力，用于倾角。"""
+
+    joint_torque: np.ndarray
+    """``(T, N, J)`` 实际施加的关节力矩。"""
+
+    joint_vel: np.ndarray
+    """``(T, N, J)`` 关节速度。"""
+
+    feet_pos_w: np.ndarray
+    """``(T, N, F, 2)`` 足端/轮心的世界 xy，用于支撑多边形。"""
+
+    support_force_n: np.ndarray
+    """``(T, N, S)`` 支撑体的法向合力。"""
+
+    support_contact_time_s: np.ndarray
+    """``(T, N, S)`` 支撑体的连续接触时长。"""
+
+    done: np.ndarray
+    """``(T, N)`` 每步之后的终止标志（1.0 表示该步结束后被重置）。"""
+
+    mass_kg: float | None = None
+    """实际使用的整机质量 [kg]。``None`` 表示上游未能从资产读到。"""
+
+    def __post_init__(self) -> None:
+        self.validate()
+
+    @property
+    def num_frames(self) -> int:
+        return int(self.base_pos_w.shape[0])
+
+    @property
+    def num_steps(self) -> int:
+        """实际执行的控制步数（帧数减一）。"""
+        return self.num_frames - 1
+
+    @property
+    def num_envs(self) -> int:
+        return int(self.base_pos_w.shape[1])
+
+    @property
+    def num_joints(self) -> int:
+        return int(self.joint_torque.shape[2])
+
+    def validate(self) -> None:
+        """形状一致性检查。早失败好过算出一堆形状广播出来的错数字。"""
+        frames, envs = self.num_frames, self.num_envs
+        if frames < 2:
+            raise ValueError(f"Trace 至少需要 2 帧（起始帧 + 至少一步），收到 {frames} 帧。")
+
+        expected: dict[str, tuple[int, ...]] = {
+            "base_pos_w": (frames, envs, 3),
+            "base_lin_vel_w": (frames, envs, 3),
+            "base_lin_vel_b": (frames, envs, 3),
+            "base_ang_vel_b": (frames, envs, 3),
+            "projected_gravity_b": (frames, envs, 3),
+            "done": (frames, envs),
+        }
+        for name, shape in expected.items():
+            array = np.asarray(getattr(self, name))
+            if array.shape != shape:
+                raise ValueError(f"{name} 形状应为 {shape}，实际 {array.shape}。")
+
+        joints = self.num_joints
+        for name in ("joint_torque", "joint_vel"):
+            if np.asarray(getattr(self, name)).shape != (frames, envs, joints):
+                raise ValueError(
+                    f"{name} 形状应为 {(frames, envs, joints)}，实际 {np.asarray(getattr(self, name)).shape}。"
+                )
+
+        feet = np.asarray(self.feet_pos_w)
+        if feet.shape[:2] != (frames, envs) or feet.ndim != 4 or feet.shape[3] != 2:
+            raise ValueError(f"feet_pos_w 形状应为 {(frames, envs, feet.shape[2] if feet.ndim > 2 else 0, 2)}，"
+                             f"实际 {feet.shape}。")
+
+        for name in ("support_force_n", "support_contact_time_s"):
+            array = np.asarray(getattr(self, name))
+            if array.ndim != 3 or array.shape[:2] != (frames, envs):
+                raise ValueError(f"{name} 形状应为 {(frames, envs, array.shape[2] if array.ndim > 2 else 0)}，"
+                                 f"实际 {array.shape}。")
+
+        if np.asarray(self.command).shape != (3,):
+            raise ValueError(f"command 应为 (3,)，实际 {np.asarray(self.command).shape}。")
+
+    def save(self, path: str) -> None:
+        """把轨迹存成 ``.npz``，便于事后用新指标重新分析，无需重跑仿真。"""
+        np.savez_compressed(
+            path,
+            dt=np.asarray(self.dt),
+            command=self.command,
+            base_pos_w=self.base_pos_w,
+            base_lin_vel_w=self.base_lin_vel_w,
+            base_lin_vel_b=self.base_lin_vel_b,
+            base_ang_vel_b=self.base_ang_vel_b,
+            projected_gravity_b=self.projected_gravity_b,
+            joint_torque=self.joint_torque,
+            joint_vel=self.joint_vel,
+            feet_pos_w=self.feet_pos_w,
+            support_force_n=self.support_force_n,
+            support_contact_time_s=self.support_contact_time_s,
+            done=self.done,
+            mass_kg=np.asarray(-1.0 if self.mass_kg is None else self.mass_kg),
+        )
+
+    @classmethod
+    def load(cls, path: str) -> Trace:
+        with np.load(path) as data:
+            mass = float(data["mass_kg"])
+            return cls(
+                dt=float(data["dt"]),
+                command=data["command"],
+                base_pos_w=data["base_pos_w"],
+                base_lin_vel_w=data["base_lin_vel_w"],
+                base_lin_vel_b=data["base_lin_vel_b"],
+                base_ang_vel_b=data["base_ang_vel_b"],
+                projected_gravity_b=data["projected_gravity_b"],
+                joint_torque=data["joint_torque"],
+                joint_vel=data["joint_vel"],
+                feet_pos_w=data["feet_pos_w"],
+                support_force_n=data["support_force_n"],
+                support_contact_time_s=data["support_contact_time_s"],
+                done=data["done"],
+                mass_kg=None if mass < 0.0 else mass,
+            )
diff --git a/eval_kit/core/terrain_spec.py b/eval_kit/core/terrain_spec.py
new file mode 100644
index 0000000..d94164c
--- /dev/null
+++ b/eval_kit/core/terrain_spec.py
@@ -0,0 +1,157 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""地形等级规格：纯数据，与仿真框架无关。
+
+规格描述「评估用哪几种地形、每种地形分几级、每级多高」，用绝对高度表示，
+因为机器人越障能力本来就是由绝对高度决定的。
+
+规格本身不含任何框架信息；「把规格翻译成本框架的地形生成器参数」这件事
+由各框架的 builder 负责（当前是 :mod:`eval_kit.isaaclab.env_setup`）。
+换框架时改 builder，不改这里的数值。
+
+数值与 ``scripts/reinforcement_learning/rsl_rl/eval.py`` 的 ``TERRAIN_LEVELS``
+保持一致，因此两套评估所用地形严格相同。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Mapping
+
+#: 无量纲坡度：高 / 水平距离。0.40 相当于 21.8°。
+_UNIT_SLOPE = "slope (rise/run, 0.40 ≈ 21.8°)"
+#: 台阶高度，米。
+_UNIT_METER = "m"
+
+
+@dataclass(frozen=True)
+class TerrainSpec:
+    """一套地形评估规格。"""
+
+    name: str
+    """规格名，写进输出 JSON。"""
+
+    types: tuple[str, ...]
+    """参与评估的地形类型，同时也是上限矩阵的列顺序。"""
+
+    levels: Mapping[str, tuple[float, ...]]
+    """每种地形的难度等级值（绝对量，单位见 :attr:`unit`），由易到难。"""
+
+    unit: Mapping[str, str]
+    """每种地形等级值的单位说明。"""
+
+    isaaclab_subterrain: Mapping[str, str]
+    """地形类型 → IsaacLab ``ROUGH_TERRAINS_CFG`` 里的子地形名。"""
+
+    isaaclab_param: Mapping[str, str]
+    """地形类型 → 需要在子地形配置里写入的字段名。"""
+
+    feature_scaling: Mapping[str, tuple[float, float]] = field(default_factory=dict)
+    """瓦片尺寸缩小时的特征尺寸上限：``字段名 -> (相对瓦片的比例, 绝对上限)``。
+
+    例如 ``{"step_width": (0.125, 0.25)}`` 表示 ``step_width = min(0.25, tile * 0.125)``。
+    这样同一套规格可以用在不同大小的地形瓦片上。
+    """
+
+    grid_rows: int = 5
+    grid_cols: int = 5
+    """非上限评估时地形生成器的网格规模。"""
+
+    isaaclab_extra_params: Mapping[str, Mapping[str, float]] = field(default_factory=dict)
+    """地形类型 → 需要额外写入的固定字段，例如 ``{"rough_slope": {"noise_step": 0.02}}``。
+
+    与 :attr:`isaaclab_param` 的区别：后者写的是随等级变化的首尾值，这里写的是与等级
+    无关的固定量。写入时会校验字段是否存在，写错会直接报错而不是静默造一个假属性。
+    """
+
+    def levels_for(self, terrain_type: str, num_levels: int | None = None) -> tuple[float, ...]:
+        """取某种地形的等级值，``num_levels`` 为 None 时取全部。"""
+        if terrain_type not in self.levels:
+            raise KeyError(f"地形规格 {self.name!r} 里没有 {terrain_type!r}；可选：{sorted(self.levels)}")
+        values = tuple(self.levels[terrain_type])
+        if num_levels is None:
+            return values
+        if num_levels > len(values):
+            raise ValueError(
+                f"地形 {terrain_type!r} 只定义了 {len(values)} 级难度，请求了 {num_levels} 级。"
+            )
+        return values[:num_levels]
+
+    def scaling_for(self, field_name: str, tile_size_m: float) -> float | None:
+        """按瓦片尺寸算某个特征字段的上限值，未定义则返回 None。"""
+        rule = self.feature_scaling.get(field_name)
+        if rule is None:
+            return None
+        ratio, cap = rule
+        return min(cap, tile_size_m * ratio)
+
+    def to_dict(self) -> dict[str, object]:
+        return {
+            "name": self.name,
+            "types": list(self.types),
+            "levels": {key: list(value) for key, value in self.levels.items()},
+            "unit": dict(self.unit),
+        }
+
+
+STANDARD_V1 = TerrainSpec(
+    name="standard_v1",
+    types=("stairs_up", "stairs_down", "smooth_slope", "rough_slope", "boxes"),
+    levels={
+        "stairs_up": (0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18),
+        "stairs_down": (0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18),
+        "smooth_slope": (0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40),
+        # 注意：random_rough 用的是 HfRandomUniformTerrainCfg，IsaacLab 明确写了
+        # 「difficulty 参数被忽略」，所以这里只有首尾值真正起作用 —— 写成
+        # noise_range=(levels[0], levels[-1])，是**绝对高度范围**而非起伏幅度。
+        # 旧 eval.py 把它设成 (0.05, 0.40)，在 2 cm 水平网格上等于 40 cm 高的随机尖刺，
+        # 轮半径 0.096 m 的机器人无法通过，那一列几乎恒为 0（实测只从 0.12 升到 0.45）。
+        # 这里回到与训练地形同量级的范围（上游训练用 noise_range=(0.02, 0.10)）。
+        "rough_slope": (0.02, 0.03, 0.04, 0.05, 0.06, 0.08, 0.09, 0.10),
+        "boxes": (0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18),
+    },
+    unit={
+        "stairs_up": _UNIT_METER,
+        "stairs_down": _UNIT_METER,
+        "smooth_slope": _UNIT_SLOPE,
+        "rough_slope": _UNIT_METER + "（高度范围；此地形忽略难度参数，8 行同分布，等级排名无意义）",
+        "boxes": _UNIT_METER,
+    },
+    isaaclab_subterrain={
+        "stairs_up": "pyramid_stairs",
+        "stairs_down": "pyramid_stairs_inv",
+        "smooth_slope": "hf_pyramid_slope",
+        "rough_slope": "random_rough",
+        "boxes": "boxes",
+    },
+    isaaclab_param={
+        "stairs_up": "step_height_range",
+        "stairs_down": "step_height_range",
+        "smooth_slope": "slope_range",
+        "rough_slope": "noise_range",
+        "boxes": "grid_height_range",
+    },
+    feature_scaling={
+        "platform_width": (0.2, 0.4),
+        "border_width": (0.05, 0.1),
+        "step_width": (0.125, 0.25),
+        "grid_width": (0.15, 0.3),
+    },
+    # 上游 ROUGH_TERRAINS_CFG 用 noise_step=0.02；旧 eval.py 把它改成 0.01 后又叠加
+    # noise_range 上限 0.40 m，相当于 1 cm 水平网格上放 40 cm 起伏 —— 极端尖锐的尖刺，
+    # 轮半径只有 0.096 m 的机器人基本无法通过。这里回到上游的 0.02。
+    isaaclab_extra_params={
+        "rough_slope": {"noise_step": 0.02},
+    },
+    grid_rows=5,
+    grid_cols=5,
+)
+
+TERRAIN_SPECS: dict[str, TerrainSpec] = {STANDARD_V1.name: STANDARD_V1}
+
+
+def get_terrain_spec(name: str) -> TerrainSpec:
+    if name not in TERRAIN_SPECS:
+        raise KeyError(f"未知地形规格 {name!r}；可选：{sorted(TERRAIN_SPECS)}")
+    return TERRAIN_SPECS[name]
diff --git a/eval_kit/isaaclab/__init__.py b/eval_kit/isaaclab/__init__.py
new file mode 100644
index 0000000..6604867
--- /dev/null
+++ b/eval_kit/isaaclab/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""IsaacLab 适配层：唯一接触仿真环境内部的地方。
+
+这一层**依赖** isaaclab / isaacsim，因此必须在 ``AppLauncher`` 启动之后才能 import。
+上面的 :mod:`eval_kit.core` 与 :mod:`eval_kit.profiles` 不依赖它，可以单独使用。
+
+职责边界：
+
+- :mod:`eval_kit.isaaclab.env_setup` —— 把协议与地形规格翻译成 env_cfg 的改动
+- :mod:`eval_kit.isaaclab.recorder`  —— 跑 rollout 并采集成 :class:`~eval_kit.core.schema.Trace`
+- :mod:`eval_kit.isaaclab.policy`    —— checkpoint 解析与策略加载
+
+这里没有任何指标计算逻辑，指标全在内核里。
+"""
+
+__all__ = ["env_setup", "policy", "recorder"]
diff --git a/eval_kit/isaaclab/env_setup.py b/eval_kit/isaaclab/env_setup.py
new file mode 100644
index 0000000..26fee2c
--- /dev/null
+++ b/eval_kit/isaaclab/env_setup.py
@@ -0,0 +1,307 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""把评估协议与地形规格翻译成 IsaacLab 的 ``env_cfg`` 改动。
+
+这些函数对应旧 ``eval.py`` 里的 ``apply_eval_terrain_baseline`` /
+``configure_eval_env`` / ``configure_upper_limit_env`` / ``assign_upper_limit_origins``，
+区别是：
+
+- 数值全部来自 :class:`~eval_kit.core.terrain_spec.TerrainSpec` 与
+  :class:`~eval_kit.core.schema.EvalProtocol`，没有散落的常数
+- 需要关掉的随机化事件分「通用」与「机器人相关」两组，机器人相关的那组用
+  ``hasattr`` 守卫，因此换机器人不会因为字段不存在而报错
+"""
+
+from __future__ import annotations
+
+import copy
+from typing import Any, Sequence
+
+import torch
+
+from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG
+
+from eval_kit.core.schema import EvalProtocol
+from eval_kit.core.terrain_spec import TerrainSpec
+
+#: 所有任务都需要的通用随机化项。
+COMMON_RANDOMIZATION_TERMS: tuple[str, ...] = (
+    "randomize_rigid_body_material",
+    "randomize_rigid_body_mass",
+    "randomize_rigid_body_mass_base",
+    "randomize_rigid_body_inertia",
+    "randomize_com_positions",
+    "randomize_apply_external_force_torque",
+    "randomize_actuator_gains",
+)
+
+#: 火蚁专属的随机化项（机身/膝部与轮胎分开做材质随机化）。
+ROBOT_SPECIFIC_RANDOMIZATION_TERMS: tuple[str, ...] = (
+    "randomize_wheel_material",
+    "randomize_support_material",
+)
+
+
+def set_fixed_command(env_cfg: Any, protocol: EvalProtocol) -> None:
+    """把速度指令钉成一个恒定值，并在整个 episode 内不重采样。"""
+    command_cfg = env_cfg.commands.base_velocity
+    command_x, command_y, command_yaw = protocol.command
+    command_cfg.heading_command = False
+    command_cfg.resampling_time_range = (
+        protocol.steps * protocol.dt + 0.01,
+        protocol.steps * protocol.dt + 0.01,
+    )
+    command_cfg.ranges.lin_vel_x = (command_x, command_x)
+    command_cfg.ranges.lin_vel_y = (command_y, command_y)
+    command_cfg.ranges.ang_vel_z = (command_yaw, command_yaw)
+    command_cfg.debug_vis = False
+
+
+def apply_eval_terrain_baseline(env_cfg: Any, spec: TerrainSpec, protocol: EvalProtocol) -> None:
+    """所有任务统一用同一套评估地形，保证不同机器人/不同实验面对的是同一块地。"""
+    if env_cfg.scene.terrain.terrain_type == "plane":
+        env_cfg.scene.terrain.terrain_type = "generator"
+    env_cfg.scene.terrain.terrain_generator = copy.deepcopy(ROUGH_TERRAINS_CFG)
+
+    generator = env_cfg.scene.terrain.terrain_generator
+    generator.size = (protocol.terrain_size_m, protocol.terrain_size_m)
+    generator.border_width = 2.0
+    generator.curriculum = False
+    generator.num_rows = spec.grid_rows
+    generator.num_cols = spec.grid_cols
+    generator.use_cache = False
+    env_cfg.scene.terrain.max_init_terrain_level = None
+
+    # 瓦片变小的时候，台阶宽度/平台宽度等特征也要跟着收，否则一块地里塞不下
+    for sub_cfg in generator.sub_terrains.values():
+        for field_name in spec.feature_scaling:
+            if hasattr(sub_cfg, field_name):
+                setattr(sub_cfg, field_name, spec.scaling_for(field_name, protocol.terrain_size_m))
+
+
+def _build_push_event(protocol: EvalProtocol):
+    """构造周期性扰动事件。"""
+    from isaaclab.managers import EventTermCfg as EventTerm
+
+    try:
+        # 与旧 eval.py 同源，保证扰动行为完全一致
+        import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+    except ImportError:
+        from isaaclab_tasks.manager_based.locomotion.velocity import mdp
+
+    return EventTerm(
+        func=mdp.push_by_setting_velocity,
+        mode="interval",
+        interval_range_s=(protocol.push_interval_s[0], protocol.push_interval_s[1]),
+        params={
+            "velocity_range": {
+                "x": protocol.push_velocity_range,
+                "y": protocol.push_velocity_range,
+            }
+        },
+    )
+
+
+def _configure_viewer(env_cfg: Any, viewer_env_index: int | None, free_camera: bool) -> None:
+    """设置视口相机：跟随某个机器人，或交给客户端自己控制。
+
+    ``ViewportCameraController`` 只在 ``origin_type`` 是 ``asset_root`` /
+    ``asset_body`` 时每帧改写相机位姿；设成 ``world`` 只在初始化时写一次，
+    之后客户端的操作不会被抢回去 —— 这就是「自由视角」。起始视角由子进程按
+    实际地形范围另设（见 ``eval_suite._set_free_camera_overview``）。
+    """
+    viewer = getattr(env_cfg, "viewer", None)
+    if viewer is None:
+        return
+    if free_camera:
+        viewer.origin_type = "world"
+        # 仅作初始化时的临时值，正式视角在环境建好后按地形范围重设
+        viewer.eye = (0.0, -20.0, 15.0)
+        viewer.lookat = (0.0, 0.0, 0.0)
+        return
+    if viewer_env_index is None:
+        return
+    viewer.origin_type = "asset_root"
+    viewer.asset_name = "robot"
+    viewer.env_index = int(viewer_env_index)
+    # 相对机器人的偏移：斜前方 2.5 m、抬高 1.6 m，看向机身略前方
+    viewer.eye = (2.5, -2.5, 1.6)
+    viewer.lookat = (0.4, 0.0, 0.1)
+
+
+def configure_eval_env(
+    env_cfg: Any,
+    spec: TerrainSpec,
+    protocol: EvalProtocol,
+    *,
+    num_envs: int | None = None,
+    device: str | None = None,
+    seed: int | None = None,
+    viewer_env_index: int | None = None,
+    free_camera: bool = False,
+) -> None:
+    """把训练用配置改造成单次评估用配置。
+
+    关掉观测噪声、课程、随机化，把重置位姿钉死，指令固定，地形统一。
+
+    ``viewer_env_index`` 不为 None 时，把视口相机改成跟随该环境的机器人；
+    ``free_camera`` 为 True 时改成不跟随任何物体、由客户端自己移动（两者互斥，
+    前者优先）。这两种都只在有画面时（本地 GUI 或推流）才有意义 —— headless
+    且不推流时 IsaacLab 根本不创建相机控制器，设了也无害。
+    """
+    env_cfg.scene.num_envs = num_envs if num_envs is not None else env_cfg.scene.num_envs
+    env_cfg.episode_length_s = protocol.steps * protocol.dt
+    if seed is not None:
+        env_cfg.seed = seed
+    if device is not None:
+        env_cfg.sim.device = device
+
+    _configure_viewer(env_cfg, viewer_env_index, free_camera)
+
+    env_cfg.observations.policy.enable_corruption = False
+    set_fixed_command(env_cfg, protocol)
+    apply_eval_terrain_baseline(env_cfg, spec, protocol)
+
+    # 课程必须关掉，否则难度/指令会在评估过程中自己变
+    for term in ("terrain_levels", "command_levels"):
+        if getattr(env_cfg.curriculum, term, None) is not None:
+            setattr(env_cfg.curriculum, term, None)
+
+    for term in (*COMMON_RANDOMIZATION_TERMS, *ROBOT_SPECIFIC_RANDOMIZATION_TERMS):
+        if hasattr(env_cfg.events, term):
+            setattr(env_cfg.events, term, None)
+
+    # 重置位姿钉死：同一批机器人从同一个位置、同一姿态、零速度出发
+    reset_base = getattr(env_cfg.events, "randomize_reset_base", None)
+    if reset_base is not None:
+        start_x, start_y, start_yaw = protocol.start_pose
+        # z 是相对地形瓦片中心高度的抬升。默认 0.0 与旧 eval.py 一致，但只对
+        # 「瓦片中心最高」的地形成立；倒金字塔台阶这类中心最低的地形需要正值，
+        # 否则机器人在 |x|>platform 处会被生成到地形几何体内部而卡住。
+        reset_base.params = {
+            "pose_range": {
+                "x": (start_x, start_x),
+                "y": (start_y, start_y),
+                "z": (protocol.start_z_offset_m, protocol.start_z_offset_m),
+                "roll": (0.0, 0.0),
+                "pitch": (0.0, 0.0),
+                "yaw": (start_yaw, start_yaw),
+            },
+            "velocity_range": {axis: (0.0, 0.0) for axis in ("x", "y", "z", "roll", "pitch", "yaw")},
+        }
+
+    if hasattr(env_cfg.events, "randomize_push_robot"):
+        env_cfg.events.randomize_push_robot = _build_push_event(protocol) if protocol.enable_interval_pushes else None
+
+    # 越界终止项：它比较的是世界坐标与整张地图的范围，不是机器人自己那块地砖，
+    # 小地砖 + 大网格时会把最外侧格子判成越界（详见 EvalProtocol 的说明）。
+    if not protocol.enforce_terrain_bounds and getattr(env_cfg.terminations, "terrain_out_of_bounds", None) is not None:
+        env_cfg.terminations.terrain_out_of_bounds = None
+
+
+# 说明：判定地砖是否够大的逻辑已移到 eval_kit.core.schema.EvalProtocol.
+# minimum_terrain_size_for_bounds()。它只用到协议自己的 start_pose，放在适配层会
+# 让不启动仿真的编排进程被迫 import isaaclab（进而因缺 pxr 而失败）。
+
+
+def configure_upper_limit_env(
+    env_cfg: Any,
+    spec: TerrainSpec,
+    protocol: EvalProtocol,
+    terrain_types: Sequence[str],
+    *,
+    seed: int | None = None,
+    device: str | None = None,
+    viewer_env_index: int | None = None,
+    free_camera: bool = False,
+) -> None:
+    """配置上限评估的固定网格：行 = 难度等级，列 = 地形类型。
+
+    ``seed`` 同时决定地形生成与重置采样。显式传入是为了让地形本身在不同种子间
+    保持一致（同一块地），而机器人初始状态可以随种子变化。
+
+    ``viewer_env_index`` / ``free_camera`` 透传给 :func:`configure_eval_env`，
+    用于让视口相机跟随某个环境的机器人，或者干脆交给客户端自由控制。
+    """
+    configure_eval_env(
+        env_cfg, spec, protocol, seed=seed, device=device,
+        viewer_env_index=viewer_env_index, free_camera=free_camera,
+    )
+
+    generator = env_cfg.scene.terrain.terrain_generator
+    generator.curriculum = True
+    generator.num_rows = protocol.num_levels
+    generator.num_cols = len(terrain_types)
+    generator.difficulty_range = (0.0, 1.0)
+    if hasattr(generator, "seed"):
+        generator.seed = int(seed) if seed is not None else int(env_cfg.seed or 0)
+
+    # 每个格子里放 envs_per_cell 个机器人
+    env_cfg.scene.num_envs = protocol.num_levels * len(terrain_types) * protocol.envs_per_cell
+    env_cfg.scene.terrain.max_init_terrain_level = protocol.num_levels - 1
+
+    # 只保留这次评估要用到的子地形，且按被选中的次数分配占比
+    isaaclab_names = [spec.isaaclab_subterrain[terrain_type] for terrain_type in terrain_types]
+    original = generator.sub_terrains
+    generator.sub_terrains = {name: copy.deepcopy(original[name]) for name in isaaclab_names}
+    for name, sub_cfg in generator.sub_terrains.items():
+        sub_cfg.proportion = isaaclab_names.count(name) / len(isaaclab_names)
+
+    # 把等级表里的首尾值写进子地形的难度区间。
+    # 用 _set_sub_terrain_field 而不是裸 setattr：字段名写错要立刻报错，
+    # 静默造一个假属性会让地形保持默认难度，而结果看起来「只是很难」。
+    for terrain_type in terrain_types:
+        sub_cfg = generator.sub_terrains[spec.isaaclab_subterrain[terrain_type]]
+        levels = spec.levels_for(terrain_type, protocol.num_levels)
+        _set_sub_terrain_field(sub_cfg, spec.isaaclab_param[terrain_type], (levels[0], levels[-1]), terrain_type)
+
+    # 与等级无关的固定字段（例如粗糙地形的噪声步长）
+    for terrain_type, extra in spec.isaaclab_extra_params.items():
+        if terrain_type not in terrain_types:
+            continue
+        sub_cfg = generator.sub_terrains[spec.isaaclab_subterrain[terrain_type]]
+        for field_name, value in extra.items():
+            _set_sub_terrain_field(sub_cfg, field_name, value, terrain_type)
+
+
+def _set_sub_terrain_field(sub_cfg: Any, field_name: str, value: Any, terrain_type: str) -> None:
+    """写入子地形字段，字段不存在时报错而不是静默创建一个假属性。"""
+    if not hasattr(sub_cfg, field_name):
+        raise AttributeError(
+            f"地形规格为 {terrain_type!r} 指定的字段 {field_name!r} 在 "
+            f"{type(sub_cfg).__name__} 上不存在。裸 setattr 会静默造一个假属性、"
+            f"使地形保持默认难度，而那看起来只是「很难」。可用字段："
+            f"{sorted(k for k in vars(sub_cfg) if not k.startswith('_'))}"
+        )
+    setattr(sub_cfg, field_name, value)
+
+
+def assign_upper_limit_origins(
+    env: Any,
+    spec: TerrainSpec,
+    protocol: EvalProtocol,
+    terrain_types: Sequence[str],
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """给每个格子平均分配机器人，并直接把地形等级/类型/起点写进地形对象。
+
+    返回 ``(level_ids, type_ids)``，用于事后把逐环境结果折叠成矩阵。
+    """
+    base_env = env.unwrapped
+    terrain = base_env.scene.terrain
+    device = base_env.device
+
+    levels: list[int] = []
+    types: list[int] = []
+    for type_index in range(len(terrain_types)):
+        for level_index in range(protocol.num_levels):
+            levels.extend([level_index] * protocol.envs_per_cell)
+            types.extend([type_index] * protocol.envs_per_cell)
+
+    level_ids = torch.tensor(levels, dtype=torch.long, device=device)
+    type_ids = torch.tensor(types, dtype=torch.long, device=device)
+    terrain.terrain_levels = level_ids
+    terrain.terrain_types = type_ids
+    terrain.env_origins = terrain.terrain_origins[level_ids, type_ids]
+    env.reset()
+    return level_ids, type_ids
diff --git a/eval_kit/isaaclab/policy.py b/eval_kit/isaaclab/policy.py
new file mode 100644
index 0000000..2fb468b
--- /dev/null
+++ b/eval_kit/isaaclab/policy.py
@@ -0,0 +1,71 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""checkpoint 解析与策略加载（IsaacLab + RSL-RL）。"""
+
+from __future__ import annotations
+
+import os
+from typing import Any
+
+import gymnasium as gym
+
+from isaaclab.envs import (
+    DirectMARLEnv,
+    DirectMARLEnvCfg,
+    DirectRLEnvCfg,
+    ManagerBasedRLEnvCfg,
+    multi_agent_to_single_agent,
+)
+from isaaclab_tasks.utils import get_checkpoint_path
+from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper
+
+
+def resolve_checkpoint(
+    log_root_path: str,
+    load_run: str | None = None,
+    checkpoint: str | None = None,
+) -> str:
+    """解析出 checkpoint 的绝对路径。
+
+    允许 ``load_run`` / ``checkpoint`` 传绝对路径或带目录的相对路径，从而跨实验目录
+    取权重（例如从 Stage 1 的目录里取权重来评估），否则回退到 IsaacLab 的按名查找。
+    """
+    if checkpoint is not None:
+        checkpoint_path = os.path.expanduser(checkpoint)
+        if os.path.isabs(checkpoint_path) or os.path.dirname(checkpoint_path):
+            checkpoint_path = os.path.abspath(checkpoint_path)
+            if not os.path.isfile(checkpoint_path):
+                raise FileNotFoundError(f"找不到 checkpoint：{checkpoint_path}")
+            return checkpoint_path
+
+    if load_run is not None:
+        run_path = os.path.expanduser(load_run)
+        if os.path.isabs(run_path) or os.path.dirname(run_path):
+            run_path = os.path.abspath(run_path)
+            if not os.path.isdir(run_path):
+                raise FileNotFoundError(f"找不到运行目录：{run_path}")
+            return get_checkpoint_path(os.path.dirname(run_path), os.path.basename(run_path), checkpoint)
+
+    return get_checkpoint_path(log_root_path, load_run, checkpoint)
+
+
+def make_env(
+    task: str,
+    env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg,
+    clip_actions: float | None = None,
+):
+    """按已配置好的 ``env_cfg`` 建环境，并包成 RSL-RL 的向量环境。"""
+    env = gym.make(task, cfg=env_cfg)
+    if isinstance(env.unwrapped, DirectMARLEnv):
+        env = multi_agent_to_single_agent(env)
+    return RslRlVecEnvWrapper(env, clip_actions=clip_actions)
+
+
+def load_policy(env, agent_cfg: Any, resume_path: str, device: str | None = None):
+    """加载 checkpoint 并返回推理策略。"""
+    from rsl_rl.runners import OnPolicyRunner
+
+    runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=device or agent_cfg.device)
+    runner.load(resume_path)
+    return runner.get_inference_policy(device=device or env.unwrapped.device)
diff --git a/eval_kit/isaaclab/recorder.py b/eval_kit/isaaclab/recorder.py
new file mode 100644
index 0000000..341cea1
--- /dev/null
+++ b/eval_kit/isaaclab/recorder.py
@@ -0,0 +1,257 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""跑 rollout 并把仿真信号采集成框架无关的 :class:`~eval_kit.core.schema.Trace`。
+
+这是唯一读取 ``env`` 内部状态的地方。采集完之后环境就可以关掉了，后续所有
+指标计算都在内核里对纯数组进行。
+
+形态相关的下标（谁是轮、谁是支撑体、哪些关节耗电）全部由
+:class:`~eval_kit.core.schema.RobotProfile` 的正则解析得到，代码里没有硬编码的
+link/joint 名字。解析不到时记录警告并跳过对应指标，而不是抛异常。
+"""
+
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+import torch
+
+from eval_kit.core.naming import resolve_matching_indices
+from eval_kit.core.schema import EvalProtocol, RobotProfile, Trace
+
+#: 默认的接触力传感器名字，与 ``velocity_env_cfg.py`` 的场景定义一致。
+DEFAULT_CONTACT_SENSOR = "contact_forces"
+
+
+@dataclass
+class RolloutRecorder:
+    """一次评估里固定不变的东西：形态下标与质量。建一次，跑多次。"""
+
+    feet_ids: list[int] = field(default_factory=list)
+    feet_names: list[str] = field(default_factory=list)
+    support_ids: list[int] = field(default_factory=list)
+    support_names: list[str] = field(default_factory=list)
+    joint_ids: list[int] = field(default_factory=list)
+    joint_names: list[str] = field(default_factory=list)
+    mass_kg: float | None = None
+    contact_sensor_name: str = DEFAULT_CONTACT_SENSOR
+    warnings: list[str] = field(default_factory=list)
+
+    def describe(self) -> str:
+        lines = [
+            f"足端 {len(self.feet_ids)} 个: {self.feet_names or '（无）'}",
+            f"支撑体 {len(self.support_ids)} 个: {self.support_names or '（无）'}",
+            f"驱动关节 {len(self.joint_ids)} 个: {self.joint_names or '（无）'}",
+            f"整机质量: {'未知' if self.mass_kg is None else f'{self.mass_kg:.3f} kg'}",
+        ]
+        for warning in self.warnings:
+            lines.append(f"[警告] {warning}")
+        return "\n".join(lines)
+
+
+def read_total_mass(robot: Any) -> float | None:
+    """从仿真资产读整机质量。
+
+    优先读 PhysX 运行时的真实质量（会反映任何质量随机化），读不到再退回资产默认质量。
+    """
+    try:
+        masses = robot.root_physx_view.get_masses()
+        return float(masses[0].sum().item())
+    except Exception:  # noqa: BLE001 - 不同版本 API 差异，读不到就退回
+        pass
+    try:
+        return float(robot.data.default_mass[0].sum().item())
+    except Exception:  # noqa: BLE001
+        return None
+
+
+def build_recorder(
+    env: Any,
+    profile: RobotProfile,
+    *,
+    contact_sensor_name: str = DEFAULT_CONTACT_SENSOR,
+) -> RolloutRecorder:
+    """按机器人描述解析出足端/支撑体/关节下标，并读一次质量。"""
+    base_env = env.unwrapped
+    robot = base_env.scene["robot"]
+    recorder = RolloutRecorder(contact_sensor_name=contact_sensor_name)
+
+    feet = resolve_matching_indices(profile.feet_body_patterns, robot.body_names)
+    recorder.feet_ids = feet.indices
+    recorder.feet_names = feet.names
+    for pattern in feet.unmatched_patterns:
+        recorder.warnings.append(
+            f"机器人 {profile.name!r} 的足端正则 {pattern!r} 在 body_names 里没有命中，"
+            "支撑裕度指标将被跳过。"
+        )
+
+    robot_bodies = set(robot.body_names)
+    sensor = base_env.scene.sensors.get(contact_sensor_name) if base_env.scene.sensors else None
+    if sensor is None:
+        recorder.warnings.append(
+            f"场景里没有接触力传感器 {contact_sensor_name!r}，支撑接触类指标将被跳过。"
+        )
+    else:
+        support = resolve_matching_indices(profile.support_body_patterns, sensor.body_names)
+        recorder.support_ids = support.indices
+        recorder.support_names = support.names
+        missing = [name for name in support.names if name not in robot_bodies]
+        if missing:
+            recorder.warnings.append(
+                f"接触力传感器上的 {missing} 不在机器人的 body 列表里，指标可能对不上。"
+            )
+        for pattern in support.unmatched_patterns:
+            if pattern in profile.support_body_patterns:
+                recorder.warnings.append(
+                    f"机器人 {profile.name!r} 的支撑体正则 {pattern!r} 没有命中，"
+                    "支撑接触类指标将被跳过。"
+                )
+
+    joints = resolve_matching_indices(profile.actuated_joint_patterns, robot.joint_names)
+    recorder.joint_ids = joints.indices
+    recorder.joint_names = joints.names
+    if not joints.indices:
+        recorder.warnings.append(
+            "驱动关节正则一个都没命中，能耗与 COT 将无法计算（会返回 nan）。"
+        )
+
+    recorder.mass_kg = read_total_mass(robot)
+    if recorder.mass_kg is None and profile.mass_kg is None:
+        recorder.warnings.append("既读不到资产质量、描述里也没写兜底值，COT 将返回 nan。")
+
+    return recorder
+
+
+class _FrameCollector:
+    """逐帧收集张量，最后一次性转成 numpy——每帧都转会明显变慢。"""
+
+    def __init__(self) -> None:
+        self.frames: dict[str, list[np.ndarray]] = {}
+
+    def add(self, **signals: np.ndarray) -> None:
+        for key, value in signals.items():
+            self.frames.setdefault(key, []).append(value)
+
+    def stack(self) -> dict[str, np.ndarray]:
+        return {key: np.stack(values, axis=0) for key, values in self.frames.items()}
+
+
+def _capture(robot: Any, sensor: Any, recorder: RolloutRecorder, num_envs: int) -> dict[str, np.ndarray]:
+    """读一帧信号。所有张量都要 clone，否则会被后续仿真步就地覆写。"""
+
+    def to_numpy(tensor: torch.Tensor) -> np.ndarray:
+        return tensor.detach().to("cpu", torch.float64).numpy()
+
+    signals = {
+        "base_pos_w": to_numpy(robot.data.root_pos_w.clone()),
+        "base_lin_vel_w": to_numpy(robot.data.root_lin_vel_w.clone()),
+        "base_lin_vel_b": to_numpy(robot.data.root_lin_vel_b.clone()),
+        "base_ang_vel_b": to_numpy(robot.data.root_ang_vel_b.clone()),
+        "projected_gravity_b": to_numpy(robot.data.projected_gravity_b.clone()),
+        "joint_torque": to_numpy(robot.data.applied_torque[:, recorder.joint_ids].clone())
+        if recorder.joint_ids
+        else np.zeros((num_envs, 0)),
+        "joint_vel": to_numpy(robot.data.joint_vel[:, recorder.joint_ids].clone())
+        if recorder.joint_ids
+        else np.zeros((num_envs, 0)),
+        "feet_pos_w": to_numpy(robot.data.body_pos_w[:, recorder.feet_ids, :2].clone())
+        if recorder.feet_ids
+        else np.zeros((num_envs, 0, 2)),
+    }
+
+    if sensor is not None and recorder.support_ids:
+        forces = sensor.data.net_forces_w
+        contact_time = sensor.data.current_contact_time
+        if forces is None:
+            # 传感器还没更新过（第一帧），按无接触处理
+            signals["support_force_n"] = np.zeros((num_envs, len(recorder.support_ids)))
+            signals["support_contact_time_s"] = np.zeros((num_envs, len(recorder.support_ids)))
+        else:
+            selected = forces[:, recorder.support_ids, :]
+            signals["support_force_n"] = to_numpy(torch.linalg.norm(selected, dim=-1).clone())
+            signals["support_contact_time_s"] = to_numpy(
+                (contact_time[:, recorder.support_ids].clone() if contact_time is not None else torch.zeros_like(selected[..., 0]))
+            )
+    else:
+        signals["support_force_n"] = np.zeros((num_envs, 0))
+        signals["support_contact_time_s"] = np.zeros((num_envs, 0))
+
+    return signals
+
+
+def record_rollout(
+    env: Any,
+    policy: Any,
+    protocol: EvalProtocol,
+    profile: RobotProfile,
+    recorder: RolloutRecorder | None = None,
+) -> Trace:
+    """跑 ``protocol.steps`` 步，采集一条 :class:`Trace`。
+
+    第 0 帧记录动作执行之前的起始状态，之后每步记录一次动作之后的状态，
+    因此帧数比步数多一。
+    """
+    base_env = env.unwrapped
+    robot = base_env.scene["robot"]
+    if recorder is None:
+        recorder = build_recorder(env, profile)
+    sensors = base_env.scene.sensors
+    sensor = sensors.get(recorder.contact_sensor_name) if sensors else None
+
+    num_envs = int(base_env.num_envs)
+    collector = _FrameCollector()
+    initial = _capture(robot, sensor, recorder, num_envs)
+    # 起始帧还没有任何 episode 结束，置零以保证与后续帧形状一致
+    initial["done"] = np.zeros(num_envs, dtype=np.float64)
+    collector.add(**initial)
+
+    obs = env.get_observations()
+    # 终端侧进度：可视化时推流画面可能因为渲染/编码出问题而冻住，这时只有终端能
+    # 说明仿真到底有没有在推进（推进则打印继续，冻结则这里也停住）。每 10% 打一行。
+    progress_every = max(1, protocol.steps // 10)
+    started = time.monotonic()
+    for step in range(protocol.steps):
+        with torch.inference_mode():
+            actions = policy(obs)
+            if isinstance(actions, tuple):
+                actions = actions[0]
+            obs, _, dones, _ = env.step(actions)
+        frame = _capture(robot, sensor, recorder, num_envs)
+        frame["done"] = dones.detach().to("cpu", torch.float64).numpy().reshape(num_envs)
+        collector.add(**frame)
+        if (step + 1) % progress_every == 0 or step + 1 == protocol.steps:
+            print(
+                f"[eval_kit] rollout {step + 1}/{protocol.steps} 步，用时 {time.monotonic() - started:.1f} s",
+                flush=True,
+            )
+
+    arrays = collector.stack()
+    return Trace(
+        dt=float(base_env.step_dt),
+        command=np.asarray(protocol.command, dtype=np.float64),
+        mass_kg=recorder.mass_kg,
+        **arrays,
+    )
+
+
+def summarize_early_resets(trace: Trace) -> dict[str, Any]:
+    """统计 episode 中途被重置的环境数量。
+
+    中途重置会让后续帧来自新 episode，路程/进度类指标会被污染。旧
+    ``eval.py`` 没有检查这一点，所以出现过「指标看着正常其实混了两次 rollout」
+    的情况不容易发现。
+    """
+    # 最后一帧的 done 属于 episode 正常结束，不算早退
+    early = trace.done[:-1] > 0.5
+    per_env = early.sum(axis=0)
+    affected = int(np.count_nonzero(per_env > 0))
+    return {
+        "envs_with_early_reset": affected,
+        "total_early_resets": int(per_env.sum()),
+        "max_resets_single_env": int(per_env.max()) if per_env.size else 0,
+        "polluted": affected > 0,
+    }
diff --git a/eval_kit/profiles.py b/eval_kit/profiles.py
new file mode 100644
index 0000000..9201417
--- /dev/null
+++ b/eval_kit/profiles.py
@@ -0,0 +1,56 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""机器人形态注册表。
+
+**加一个机器人 = 在这里加一条记录**，评价代码不动。
+
+数值来源：火蚁的 link/joint 取自
+``source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py``；
+机身尺寸取自 ``docs/solenopsis_urdf_comparison.md`` 里记录的碰撞体尺寸。
+"""
+
+from __future__ import annotations
+
+from eval_kit.core.schema import RobotProfile
+
+SOLENOPSIS = RobotProfile(
+    name="solenopsis",
+    # None = 运行时从仿真资产读真实质量（Solenopsis.urdf 当前为 24.74 kg）。
+    # 旧 eval.py 的命令行默认值是 25.482，那是更早的 M20 简化模型名义质量，
+    # 会让 COT 偏低约 4%。
+    mass_kg=None,
+    # 四个轮就是支撑多边形的接触点。
+    feet_body_patterns=(".*_wheel",),
+    # 机身与膝部允许作为越障时的短暂支撑。
+    support_body_patterns=("base_link", ".*_knee"),
+    # 火蚁 8 个关节全部主动驱动；分两组写便于将来出现被动关节时收窄范围。
+    actuated_joint_patterns=(".*knee_joint", ".*wheel_joint"),
+    support_force_threshold_n=15.0,
+    # 机身碰撞体 0.706 x 0.310 x 0.1714 m，取长边。
+    body_length_m=0.706,
+)
+
+#: 四足对照机，仅用于验证「换机器人只改数据」这一点，尚未在仿真里跑过。
+LITE3 = RobotProfile(
+    name="lite3",
+    mass_kg=None,
+    feet_body_patterns=(".*_FOOT",),
+    # 四足没有「机身支撑」这个语义，留空 → 支撑类指标返回 nan 而不是 0。
+    support_body_patterns=(),
+    actuated_joint_patterns=(".*_joint",),
+    support_force_threshold_n=15.0,
+    # 未知，仅用于地形瓦片尺寸校验。
+    body_length_m=0.0,
+)
+
+PROFILES: dict[str, RobotProfile] = {
+    SOLENOPSIS.name: SOLENOPSIS,
+    LITE3.name: LITE3,
+}
+
+
+def get_profile(name: str) -> RobotProfile:
+    if name not in PROFILES:
+        raise KeyError(f"未知机器人描述 {name!r}；可选：{sorted(PROFILES)}")
+    return PROFILES[name]
diff --git a/eval_kit/protocols.py b/eval_kit/protocols.py
new file mode 100644
index 0000000..3ea26c4
--- /dev/null
+++ b/eval_kit/protocols.py
@@ -0,0 +1,102 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""评估协议预设。
+
+协议就是一次评估的全部「口径」。默认预设 ``standard_v1`` 的数值与旧
+``scripts/reinforcement_learning/rsl_rl/eval.py`` 的命令行默认值一致，
+所以两套评估跑出来的数字可以直接对照。
+
+要改口径，改预设或另加一个预设，不要在调用处临时传参——预设名会进
+``protocol_fingerprint``，写进输出 JSON。
+"""
+
+from __future__ import annotations
+
+from dataclasses import replace
+
+from eval_kit.core.schema import EvalProtocol
+
+#: 标准越障评估：10 s、前进 1 m/s、最多 2 m 算成功。与旧 eval.py 默认一致。
+#:
+#: **只建议用于与旧 ``eval.py`` 做对照**：它的地砖 2.0 m 会让越界终止项误判最外侧格子，
+#: 起始高度 0.0 m 会让机器人在倒金字塔台阶地形里被生成到几何体内部
+#: （``stairs_down`` 因此恒为 0）。要拿可用的数字请用 ``standard_v1_clean``。
+STANDARD_V1 = EvalProtocol(
+    steps=500,
+    dt=0.02,
+    command=(1.0, 0.0, 0.0),
+    start_pose=(-0.9, 0.0, 0.0),
+    success_distance_m=2.0,
+    tilt_threshold_rad=0.70,
+    acc_threshold_g=2.0 / 9.81,
+    energy_convention="absolute",
+    stability_margin_method="foot_bbox",
+    support_force_threshold_n=15.0,
+    enable_interval_pushes=False,
+    terrain_spec="standard_v1",
+    terrain_size_m=2.0,
+    num_levels=8,
+    envs_per_cell=1,
+    seeds=(0,),
+)
+
+#: 抗扰评估：每 2.01 s 施加一次水平速度扰动，其余同 standard_v1。
+ROBUSTNESS_V1 = replace(
+    STANDARD_V1,
+    enable_interval_pushes=True,
+    push_interval_s=(2.0, 2.01),
+    push_velocity_range=(-0.5, 0.5),
+)
+
+#: 冒烟测试：步数与网格都缩小，用于确认流水线能跑通，不要用它的数值下结论。
+#: 地形参数沿用 ``standard_v1_clean`` 的修正值，否则它会带上越界误判的假警告。
+SMOKE = replace(
+    STANDARD_V1,
+    steps=100,
+    num_levels=2,
+    envs_per_cell=1,
+    terrain_size_m=4.0,
+    start_z_offset_m=0.5,
+)
+
+#: **推荐用于机器人对比**：修掉两处会让结果失真的问题，其余口径与 ``standard_v1`` 相同，
+#: 因此数值仍可与后者对照。
+#:
+#: 1. 地砖从 2.0 m 放大到 4.0 m，消除 IsaacLab ``terrain_out_of_bounds`` 的误判
+#:    （阈值 3.8 m 由 ``EvalProtocol.minimum_terrain_size_for_bounds()`` 给出）。
+#: 2. 起始高度抬高 0.5 m，让机器人落到地形表面而不是被生成在地形内部。
+#:    实测证据：``--terrain-type stairs_down`` 单独跑，起始高度 0.0 m 时
+#:    score=0.00 / 成功率 0.05，抬高后 score 显著恢复。取 0.5 是因为倒金字塔台阶的
+#:    嵌入深度随难度递增（L1 约 0.11 m、L8 约 0.50 m），0.5 m 刚好覆盖最深的一级。
+#:    旧 ``eval.py`` 用 0.0，所以它的倒金字塔台阶那一列恒为 0，是无效的。
+STANDARD_V1_CLEAN = replace(STANDARD_V1, terrain_size_m=4.0, start_z_offset_m=0.5)
+
+#: 另一种处置方式：保留 2.0 m 地砖，直接去掉越界终止项。
+STANDARD_V1_NO_BOUNDS = replace(STANDARD_V1, enforce_terrain_bounds=False)
+
+#: 多随机种子的标准评估 —— **对外给结论时用这个**。
+#:
+#: 单次 rollout 的噪声足以淹没机器人之间的差异，所以对比必须多种子；同时它基于
+#: ``STANDARD_V1_CLEAN``（地砖 4.0 m、起始高度 +0.5 m），不带已知的 harness 偏差。
+#:
+#: 默认 ``envs_per_cell = 1``（每格 1 个机器人），因为起始位姿钉死后同格多个机器人
+#: 起始坐标完全相同、不是独立样本。样本量靠种子数而非每格机器人数量来提升 ——
+#: 每个种子会重新生成地形实例，跨种子才是独立的。8 个种子给出每格 1/8 ≈ 12.5% 的
+#: 分辨率；想要更细就显式加 ``--seeds``。
+STANDARD_V1_MULTISEED = replace(STANDARD_V1_CLEAN, seeds=(0, 100, 200, 300, 400, 500, 600, 700))
+
+PROTOCOLS: dict[str, EvalProtocol] = {
+    "standard_v1": STANDARD_V1,
+    "standard_v1_clean": STANDARD_V1_CLEAN,
+    "standard_v1_no_bounds": STANDARD_V1_NO_BOUNDS,
+    "robustness_v1": ROBUSTNESS_V1,
+    "smoke": SMOKE,
+    "standard_v1_multiseed": STANDARD_V1_MULTISEED,
+}
+
+
+def get_protocol(name: str) -> EvalProtocol:
+    if name not in PROTOCOLS:
+        raise KeyError(f"未知评估协议 {name!r}；可选：{sorted(PROTOCOLS)}")
+    return PROTOCOLS[name]
diff --git a/eval_kit/tests/test_cli.py b/eval_kit/tests/test_cli.py
new file mode 100644
index 0000000..40ddd0a
--- /dev/null
+++ b/eval_kit/tests/test_cli.py
@@ -0,0 +1,544 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""命令行入口的静态自检：不需要 Isaac Sim，也不需要 GPU。
+
+用桩替换 ``isaaclab.app``，从而可以单独加载 ``eval_suite.py``，检查参数解析与
+口径覆盖逻辑。仿真相关的部分（建环境、跑 rollout）不在覆盖范围内——那些必须真跑。
+
+运行方式（在仓库根目录）::
+
+    python3 eval_kit/tests/test_cli.py
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+import types
+from pathlib import Path
+
+if __package__ in (None, ""):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+EVAL_SUITE_PATH = PROJECT_ROOT / "scripts" / "reinforcement_learning" / "rsl_rl" / "eval_suite.py"
+
+#: ``AppLauncher.add_app_launcher_args()`` 会添加的参数名，抄自
+#: ``IsaacLab-2.3.2/source/isaaclab/isaaclab/app/app_launcher.py``。
+#: 自己的 parser 里出现任何一个都会让 argparse 抛
+#: ``ArgumentError: conflicting option string``，脚本连启动都做不到。
+#: （只有 device / headless / enable_cameras / experience / livestream /
+#: rendering_mode / xr 会被 AppLauncher 自己的显式检查拦下，--verbose / --info /
+#: --cpu 等则由 argparse 拦，所以必须把 14 个都列出来。）
+APPLAUNCHER_ARG_NAMES = (
+    "--headless",
+    "--livestream",
+    "--enable_cameras",
+    "--xr",
+    "--device",
+    "--cpu",
+    "--verbose",
+    "--info",
+    "--experience",
+    "--rendering_mode",
+    "--kit_args",
+    "--anim_recording_enabled",
+    "--anim_recording_start_time",
+    "--anim_recording_stop_time",
+)
+
+
+def _stub_isaaclab() -> None:
+    """装一个最小的 isaaclab.app 桩，只为让模块级 import 通过。
+
+    桩要复刻真实 ``AppLauncher`` 的行为，否则测试放过的正是真跑会崩的地方：
+
+    - ``add_app_launcher_args`` 会添加上面 14 个参数名
+    - 如果 parser 里已经有同名参数，它**拒绝启动**而不是静默覆盖
+    """
+    if "isaaclab.app" in sys.modules:
+        return
+
+    class AppLauncher:  # noqa: D401 - 桩
+        def __init__(self, args):
+            self.app = None
+
+        @staticmethod
+        def add_app_launcher_args(parser):
+            existing = {option for action in parser._actions for option in action.option_strings}
+            conflict = sorted(existing & set(APPLAUNCHER_ARG_NAMES))
+            if conflict:
+                raise ValueError(
+                    f"argument {conflict[0]}: conflicting option string: {conflict[0]}"
+                )
+            parser.add_argument("--headless", action="store_true", default=False)
+            parser.add_argument("--device", type=str, default=None)
+            for name in APPLAUNCHER_ARG_NAMES:
+                dest = name.lstrip("-").replace("-", "_")
+                if dest in ("headless", "device"):
+                    continue
+                parser.add_argument(name, dest=dest, nargs="?", const=True, default=None)
+            return parser
+
+    isaaclab = types.ModuleType("isaaclab")
+    app = types.ModuleType("isaaclab.app")
+    app.AppLauncher = AppLauncher  # type: ignore[attr-defined]
+    isaaclab.app = app  # type: ignore[attr-defined]
+    sys.modules["isaaclab"] = isaaclab
+    sys.modules["isaaclab.app"] = app
+
+
+def _load_eval_suite():
+    _stub_isaaclab()
+    specification = importlib.util.spec_from_file_location("eval_suite_under_test", EVAL_SUITE_PATH)
+    if specification is None or specification.loader is None:
+        raise RuntimeError(f"无法加载 {EVAL_SUITE_PATH}")
+    module = importlib.util.module_from_spec(specification)
+    specification.loader.exec_module(module)
+    return module
+
+
+def test_parser_defaults():
+    """默认值：协议预设名、机器人描述名、评估类型都在。"""
+    module = _load_eval_suite()
+    args = module._build_parser().parse_args([])
+
+    assert args.task == "Rough-Deeprobotics-M20-v0", args.task
+    assert args.profile == "solenopsis"
+    assert args.protocol == "standard_v1"
+    assert args.eval_type == "all"
+    assert args.num_envs == 250
+    assert args.terrain_type == "all"
+    # 口径覆盖项默认必须为 None，否则会静默覆盖协议预设
+    for name in (
+        "episode_length_s", "command_x", "command_y", "command_yaw", "start_x", "start_y",
+        "terrain_size", "success_distance", "tilt_threshold", "acc_threshold_g",
+        "energy_convention", "robot_mass", "num_levels", "envs_per_cell", "seeds",
+        "enforce_terrain_bounds", "start_z_offset",
+    ):
+        assert getattr(args, name) is None, f"{name} 默认应为 None，实际 {getattr(args, name)!r}"
+    return "默认值正确，口径覆盖项默认全部为 None"
+
+
+def test_no_terrain_bounds_flag():
+    """--no-terrain-bounds 关闭越界终止项，并改变指纹。"""
+    module = _load_eval_suite()
+    from eval_kit.protocols import get_protocol
+
+    preset = get_protocol("standard_v1")
+    assert preset.enforce_terrain_bounds is True, "standard_v1 应保持与 eval.py 一致（保留终止项）"
+
+    args = module._build_parser().parse_args(["--no-terrain-bounds"])
+    protocol = module._apply_overrides(preset, args)
+    assert protocol.enforce_terrain_bounds is False
+    assert protocol.fingerprint() != preset.fingerprint(), "关闭终止项必须改变指纹"
+
+    # 不带该开关时不覆盖
+    unchanged = module._apply_overrides(preset, module._build_parser().parse_args([]))
+    assert unchanged.enforce_terrain_bounds is True
+    return "开关生效且反映在指纹里"
+
+
+def test_clean_protocol_uses_big_tile():
+    """standard_v1_clean 通过放大地砖来消除越界误判，而不是关掉终止项。"""
+    from eval_kit.protocols import get_protocol
+
+    clean = get_protocol("standard_v1_clean")
+    parity = get_protocol("standard_v1")
+    no_bounds = get_protocol("standard_v1_no_bounds")
+
+    assert clean.terrain_size_m >= 3.8, f"地砖 {clean.terrain_size_m} 不足以消除误判"
+    assert clean.enforce_terrain_bounds is True, "clean 方案应保留终止项"
+    assert no_bounds.enforce_terrain_bounds is False
+    # 除地砖外其余口径与 parity 方案一致，数值仍可对照
+    assert clean.command == parity.command
+    assert clean.success_distance_m == parity.success_distance_m
+    assert clean.num_levels == parity.num_levels
+    return f"clean 地砖 {clean.terrain_size_m} m，保留终止项；no_bounds 关闭终止项"
+
+
+def test_overrides_are_opt_in():
+    """不传任何覆盖项时，协议必须与预设逐字段相同。"""
+    module = _load_eval_suite()
+    from eval_kit.protocols import get_protocol
+
+    args = module._build_parser().parse_args([])
+    preset = get_protocol("standard_v1")
+    protocol = module._apply_overrides(preset, args)
+
+    assert protocol == preset, "未传覆盖项却改变了协议"
+    assert protocol.fingerprint() == preset.fingerprint()
+    return "未传覆盖 → 协议与预设完全相同（含 fingerprint）"
+
+
+def test_overrides_apply_and_update_fingerprint():
+    """显式覆盖项生效，且指纹随之改变。"""
+    module = _load_eval_suite()
+    from eval_kit.protocols import get_protocol
+
+    args = module._build_parser().parse_args(
+        [
+            "--episode-length-s", "20",
+            "--command-x", "0.8",
+            "--command-yaw", "-0.3",
+            "--success-distance", "3.5",
+            "--tilt-threshold", "0.5",
+            "--acc-threshold-g", "0.3",
+            "--energy-convention", "signed",
+            "--terrain-size", "1.5",
+            "--num-levels", "4",
+            "--envs-per-cell", "2",
+            "--seeds", "7", "8",
+        ]
+    )
+    preset = get_protocol("standard_v1")
+    protocol = module._apply_overrides(preset, args)
+
+    assert protocol.steps == 1000, protocol.steps  # 20 s / 0.02 s
+    assert protocol.command == (0.8, 0.0, -0.3), protocol.command
+    assert protocol.success_distance_m == 3.5
+    assert protocol.tilt_threshold_rad == 0.5
+    assert abs(protocol.acc_threshold_g - 0.3) < 1e-12
+    assert protocol.energy_convention == "signed"
+    assert protocol.terrain_size_m == 1.5
+    assert protocol.num_levels == 4
+    assert protocol.envs_per_cell == 2
+    assert protocol.seeds == (7, 8)
+    assert protocol.fingerprint() != preset.fingerprint()
+    # 没覆盖的字段保持预设
+    assert protocol.start_pose == preset.start_pose
+    assert protocol.dt == preset.dt
+    return "11 项覆盖全部生效，未覆盖字段保持预设"
+
+
+def test_command_overrides_compose_independently():
+    """单独覆盖某一个速度分量时，另两个分量保持预设。"""
+    module = _load_eval_suite()
+    from eval_kit.protocols import get_protocol
+
+    preset = get_protocol("standard_v1")  # command = (1.0, 0.0, 0.0)
+
+    only_y = module._apply_overrides(preset, module._build_parser().parse_args(["--command-y", "0.5"]))
+    assert only_y.command == (1.0, 0.5, 0.0), only_y.command
+
+    only_yaw = module._apply_overrides(preset, module._build_parser().parse_args(["--command-yaw", "1.2"]))
+    assert only_yaw.command == (1.0, 0.0, 1.2), only_yaw.command
+
+    both = module._apply_overrides(
+        preset, module._build_parser().parse_args(["--command-y", "0.5", "--command-yaw", "1.2"])
+    )
+    assert both.command == (1.0, 0.5, 1.2), both.command
+
+    only_x = module._apply_overrides(preset, module._build_parser().parse_args(["--command-x", "2.0"]))
+    assert only_x.command == (2.0, 0.0, 0.0), only_x.command
+    return "command 三个分量可独立覆盖"
+
+
+def test_start_pose_overrides_compose():
+    """起始位姿同理。"""
+    module = _load_eval_suite()
+    from eval_kit.protocols import get_protocol
+
+    preset = get_protocol("standard_v1")
+    protocol = module._apply_overrides(
+        preset, module._build_parser().parse_args(["--start-x", "-1.2", "--start-y", "0.4"])
+    )
+    assert protocol.start_pose == (-1.2, 0.4, 0.0), protocol.start_pose
+    return f"start_pose = {protocol.start_pose}"
+
+
+def test_start_z_offset_override():
+    """起始高度偏移：默认 0.0（与旧 eval.py 一致），显式给出时生效并改变指纹。"""
+    module = _load_eval_suite()
+    from eval_kit.protocols import get_protocol
+
+    preset = get_protocol("standard_v1")
+    assert preset.start_z_offset_m == 0.0, "默认必须为 0.0，否则与 eval.py 不可比"
+
+    parser = module._build_parser()
+    unchanged = module._apply_overrides(preset, parser.parse_args([]))
+    assert unchanged.start_z_offset_m == 0.0
+    assert unchanged.fingerprint() == preset.fingerprint()
+
+    lifted = module._apply_overrides(preset, parser.parse_args(["--start-z-offset", "0.3"]))
+    assert lifted.start_z_offset_m == 0.3
+    assert lifted.fingerprint() != preset.fingerprint(), "抬高起始高度必须改变指纹"
+    # 只动高度，不动水平位姿
+    assert lifted.start_pose == preset.start_pose
+    return "默认 0.0，--start-z-offset 生效且反映在指纹里"
+
+
+def test_terrain_types_resolution():
+    """地形类型：all 展开成规格里的全部类型，单个类型要校验存在性。"""
+    module = _load_eval_suite()
+    from eval_kit.core.terrain_spec import get_terrain_spec
+
+    spec = get_terrain_spec("standard_v1")
+
+    all_args = module._build_parser().parse_args([])
+    assert module._terrain_types_for(all_args, spec) == list(spec.types)
+
+    single_args = module._build_parser().parse_args(["--terrain-type", "boxes"])
+    assert module._terrain_types_for(single_args, spec) == ["boxes"]
+
+    bad_args = module._build_parser().parse_args(["--terrain-type", "nonexistent"])
+    try:
+        module._terrain_types_for(bad_args, spec)
+    except SystemExit as exc:
+        assert "nonexistent" in str(exc)
+        return "all / 单类型 / 非法类型校验都正确"
+    raise AssertionError("非法地形类型没有报错")
+
+
+def test_all_profiles_and_protocols_are_loadable():
+    """每个机器人描述、每个协议预设都要能被命令行选中。"""
+    module = _load_eval_suite()
+    parser = module._build_parser()
+    from eval_kit.profiles import PROFILES
+    from eval_kit.protocols import PROTOCOLS
+
+    for name in PROFILES:
+        args = parser.parse_args(["--profile", name])
+        assert args.profile == name
+    for name in PROTOCOLS:
+        args = parser.parse_args(["--protocol", name])
+        protocol = module._apply_overrides(PROTOCOLS[name], args)
+        assert protocol.steps >= 1
+        assert protocol.seeds, f"协议 {name} 没有种子"
+    return f"{len(PROFILES)} 个描述、{len(PROTOCOLS)} 个协议预设都可加载"
+
+
+def test_eval_types_match_report_module():
+    """命令行的评估类型必须与指标筛选表一致，避免出现选了却没指标的项。"""
+    module = _load_eval_suite()
+    from eval_kit.core.report import EVAL_TYPES, METRIC_SELECTION
+
+    args = module._build_parser().parse_args([])
+    choices = args.eval_type
+    assert choices in EVAL_TYPES
+    for eval_type in EVAL_TYPES:
+        if eval_type in ("all", "upper_limits"):
+            continue
+        assert eval_type in METRIC_SELECTION, f"{eval_type} 没有对应的指标筛选规则"
+    return f"{len(EVAL_TYPES)} 个评估类型，与 METRIC_SELECTION 对齐"
+
+
+def test_parser_compatible_with_app_launcher():
+    """自身的参数不能和 AppLauncher 冲突。
+
+    main() 的顺序是「先 _build_parser()，再 AppLauncher.add_app_launcher_args()」。
+    这里复刻同样的顺序：任何冲突都会在这一步抛错，也就是脚本连启动都做不到。
+    真跑时曾两次撞上这个坑（先是 --device、后是 --verbose），所以这个检查必须留下。
+    """
+    module = _load_eval_suite()
+    parser = module._build_parser()
+    parser = module.AppLauncher.add_app_launcher_args(parser)
+    args = parser.parse_args([])
+
+    # --device / --headless 由 AppLauncher 提供，自己的 parser 里不能再定义
+    assert args.device is None
+    assert args.headless is False
+    assert args.task == "Rough-Deeprobotics-M20-v0"
+    assert args.num_envs == 250
+    return "与 AppLauncher 的 14 个参数名无冲突，--device/--headless 由其提供"
+
+
+def test_no_option_string_collisions_with_app_launcher():
+    """直接枚举检查：自身 parser 的任何 option string 都不能落在 AppLauncher 的名单里。"""
+    module = _load_eval_suite()
+    parser = module._build_parser()
+    own = {option for action in parser._actions for option in action.option_strings}
+    conflicts = sorted(own & set(APPLAUNCHER_ARG_NAMES))
+    assert not conflicts, (
+        f"与 AppLauncher 冲突: {conflicts} —— 真实启动时会抛 "
+        "argparse.ArgumentError: conflicting option string"
+    )
+    return f"自身 {len(own)} 个 option string 与 AppLauncher 的 {len(APPLAUNCHER_ARG_NAMES)} 个均不冲突"
+
+
+def test_verbose_terrain_flag_replaces_verbose():
+    """--verbose 归 AppLauncher，自己的逐格调试用 --verbose-terrain。"""
+    module = _load_eval_suite()
+    parser = module._build_parser()
+    args = parser.parse_args(["--verbose-terrain"])
+    assert args.verbose_terrain is True
+    plain = parser.parse_args([])
+    assert plain.verbose_terrain is False
+    return "--verbose-terrain 生效，未占用 --verbose"
+
+
+def test_output_path_defaults_next_to_checkpoint():
+    """默认输出落在 checkpoint 同目录的 eval_kit/ 下，不覆盖 eval.py 的 eval/。"""
+    module = _load_eval_suite()
+    import tempfile
+
+    with tempfile.TemporaryDirectory() as directory:
+        checkpoint = str(Path(directory) / "model_100.pt")
+        Path(checkpoint).write_bytes(b"")
+        args = module._build_parser().parse_args([])
+        path = module._resolve_output_path(args, checkpoint, "all")
+
+    assert "/eval_kit/" in path, path
+    assert path.endswith(".json")
+    assert "all_" in Path(path).name
+    return f"默认输出目录为 <checkpoint 目录>/eval_kit/"
+
+
+def test_livestream_value_falls_back_to_env_var():
+    """--livestream 默认 -1 表示「听 LIVESTREAM 环境变量」，不能当成 0。"""
+    import os
+
+    module = _load_eval_suite()
+    parser = module._build_parser()
+
+    args = parser.parse_args([])
+    args.livestream = -1
+    previous = os.environ.get("LIVESTREAM")
+    os.environ.pop("LIVESTREAM", None)
+    try:
+        assert module._livestream_value(args) == 0, "没设环境变量时应视为关闭"
+        os.environ["LIVESTREAM"] = "2"
+        assert module._livestream_value(args) == 2, "应回退读 LIVESTREAM 环境变量"
+
+        explicit = parser.parse_args([])
+        explicit.livestream = 1
+        assert module._livestream_value(explicit) == 1, "命令行显式值优先于环境变量"
+    finally:
+        if previous is None:
+            os.environ.pop("LIVESTREAM", None)
+        else:
+            os.environ["LIVESTREAM"] = previous
+    return "命令行 > LIVESTREAM 环境变量 > 关闭"
+
+
+def test_viewer_env_index_covers_livestream():
+    """推流也要设视口相机；真 headless（且不推流）才返回 None。
+
+    这条决定推流画面能不能看到机器人：``AppLauncher`` 在开启推流时会把
+    ``headless`` 改写成 True，所以不能按 ``headless`` 判断「用户要不要看画面」。
+    """
+    module = _load_eval_suite()
+    # --headless 由 AppLauncher 提供，所以这里要按 main() 的顺序加上
+    parser = module.AppLauncher.add_app_launcher_args(module._build_parser())
+
+    plain_headless = parser.parse_args(["--headless"])
+    plain_headless.livestream = 0
+    assert module._viewer_env_index(plain_headless) is None, "headless 且不推流时不应设视口相机"
+
+    streamed = parser.parse_args(["--headless", "--view-env", "7"])
+    streamed.livestream = 2
+    assert module._viewer_env_index(streamed) == 7, "推流时视口相机应生效并跟随 --view-env"
+
+    default_target = parser.parse_args([])
+    default_target.livestream = 2
+    assert module._viewer_env_index(default_target) == 0, "不传 --view-env 时默认跟随 env 0"
+
+    gui = parser.parse_args([])
+    gui.livestream = 0
+    assert module._viewer_env_index(gui) == 0, "本地 GUI（非 headless）仍然跟随 env 0"
+
+    # 自由视角：不跟随，且 --free-camera 优先于 --view-env
+    free = parser.parse_args(["--headless", "--free-camera"])
+    free.livestream = 2
+    assert module._viewer_env_index(free) is None, "--free-camera 时不应跟随任何环境"
+    both = parser.parse_args(["--headless", "--free-camera", "--view-env", "3"])
+    both.livestream = 2
+    assert module._viewer_env_index(both) is None, "--free-camera 与 --view-env 同时给出时以前者为准"
+    return "headless→None；推流 / 本地 GUI→跟随 --view-env（默认 0）；--free-camera 优先于跟随"
+
+
+def test_free_camera_only_applies_with_a_picture():
+    """--free-camera 只在有画面时生效；给定 --view-env 也让位于它。"""
+    module = _load_eval_suite()
+    parser = module.AppLauncher.add_app_launcher_args(module._build_parser())
+
+    headless = parser.parse_args(["--headless", "--free-camera"])
+    headless.livestream = 0
+    assert module._display_enabled(headless) is False, "headless 且不推流时没有画面"
+    assert module._viewer_env_index(headless) is None
+
+    streamed = parser.parse_args(["--free-camera"])
+    streamed.livestream = 2
+    assert module._display_enabled(streamed) is True, "推流算有画面"
+    assert module._viewer_env_index(streamed) is None, "有画面 + --free-camera → 不跟随"
+
+    # 显式给出 --view-env 但没有 --free-camera：仍然跟随
+    following = parser.parse_args(["--view-env", "2"])
+    following.livestream = 2
+    assert module._viewer_env_index(following) == 2
+    return "无画面时 --free-camera 无效果；有画面时不跟随；未给该参数时行为不变"
+
+
+def test_pause_defaults_to_connection_window_only_when_streaming():
+    """--pause-s 是给推流客户端留连接时间的，默认值在 main() 里按是否推流决定。"""
+    module = _load_eval_suite()
+    # --livestream 由 AppLauncher 提供，所以这里要按 main() 的顺序加上
+    parser = module.AppLauncher.add_app_launcher_args(module._build_parser())
+
+    assert parser.parse_args([]).pause_s is None, "默认必须是 None，否则区分不出用户显式传的 0"
+    assert parser.parse_args(["--pause-s", "0"]).pause_s == 0.0
+    assert parser.parse_args(["--pause-s", "12.5"]).pause_s == 12.5
+
+    def resolve(arguments):
+        """复刻 main() 里的取值规则。"""
+        parsed = parser.parse_args(arguments)
+        streaming = module._livestream_value(parsed) > 0
+        return parsed.pause_s if parsed.pause_s is not None else (30.0 if streaming else 0.0)
+
+    assert resolve([]) == 0.0, "不推流时不该停顿"
+    assert resolve(["--livestream", "2"]) == 30.0, "推流时默认留 30 s 连接时间"
+    assert resolve(["--livestream", "2", "--pause-s", "0"]) == 0.0, "显式 0 可以关掉停顿"
+    assert resolve(["--livestream", "1"]) == 30.0, "公网推流同样要留连接时间"
+    return "不推流默认 0 s；推流默认 30 s；显式值优先"
+
+
+def test_local_ipv4_addresses_skips_loopback():
+    """打印给 WebRTC 客户端的地址里不能出现回环地址。"""
+    module = _load_eval_suite()
+    addresses = module._local_ipv4_addresses()
+    assert all(not address.startswith("127.") for address in addresses), addresses
+    assert all(address.count(".") == 3 for address in addresses), addresses
+    return f"拿到 {len(addresses)} 个非回环地址：{addresses}"
+
+
+def test_record_frames_defaults_off():
+    """抓帧是可选功能：默认不抓，给了目录和间隔才生效。"""
+    module = _load_eval_suite()
+    parser = module._build_parser()
+
+    plain = parser.parse_args([])
+    assert plain.record_frames is None, "默认不应抓帧"
+    assert plain.record_every == 5, "默认每 5 步一张（0.1 s 仿真时间，合成 10 fps 视频）"
+
+    on = parser.parse_args(["--record-frames", "/tmp/frames", "--record-every", "1"])
+    assert on.record_frames == "/tmp/frames"
+    assert on.record_every == 1
+    return "默认关闭；--record-frames 指定目录、--record-every 指定间隔"
+
+
+def main() -> int:
+    tests = [value for name, value in sorted(globals().items()) if name.startswith("test_")]
+    failures: list[tuple[str, str]] = []
+    for test in tests:
+        name = test.__name__
+        try:
+            note = test()
+        except (Exception, SystemExit) as exc:  # noqa: BLE001
+            # 也要接住 SystemExit：argparse 参数写错会直接 SystemExit(2)，
+            # 不接的话整个测试文件当场退出，后面的测试一条都不跑、也没有汇总行。
+            failures.append((name, f"{type(exc).__name__}: {exc}"))
+            print(f"FAIL  {name}\n        {type(exc).__name__}: {exc}")
+        else:
+            print(f"ok    {name}" + (f"  —  {note}" if note else ""))
+
+    print()
+    if failures:
+        print(f"{len(failures)}/{len(tests)} 个测试失败")
+        return 1
+    print(f"全部 {len(tests)} 个测试通过")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/eval_kit/tests/test_core.py b/eval_kit/tests/test_core.py
new file mode 100644
index 0000000..3da4229
--- /dev/null
+++ b/eval_kit/tests/test_core.py
@@ -0,0 +1,645 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""评价内核的单元测试：用合成轨迹验证指标，不需要仿真环境。
+
+运行方式（在仓库根目录）::
+
+    python3 eval_kit/tests/test_core.py
+
+合成轨迹的价值在于：指标的期望值可以手算，因此能证明指标实现是对的，
+而不是「跑起来没报错」。这也是把指标做成纯函数的主要理由。
+"""
+
+from __future__ import annotations
+
+import math
+import sys
+from dataclasses import replace
+from pathlib import Path
+
+import numpy as np
+
+if __package__ in (None, ""):
+    sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+from eval_kit.core.aggregation import (
+    accumulated_success,
+    aggregate_runs,
+    build_upper_limit_matrix,
+    upper_limit_score,
+)
+from eval_kit.core.metrics import compute_metrics, support_margin_xy, tilt_from_projected_gravity
+from eval_kit.core.naming import resolve_matching_indices
+from eval_kit.core.report import (
+    filter_metrics,
+    format_aggregate_summary,
+    format_matrix,
+    format_metric_result,
+    format_numpy_matrix,
+    format_rollout_summary,
+    format_upper_limit_summary,
+)
+from eval_kit.core.schema import EvalProtocol, RobotProfile, Trace
+from eval_kit.core.terrain_spec import get_terrain_spec
+from eval_kit.profiles import get_profile
+from eval_kit.protocols import get_protocol
+
+G = 9.81
+
+
+# --------------------------------------------------------------------------- 构造器
+
+
+def make_trace(
+    *,
+    num_envs: int = 3,
+    steps: int = 10,
+    dt: float = 0.02,
+    speed: float = 1.0,
+    tilt_rad: float = 0.0,
+    torque: float = 1.0,
+    joint_vel: float = 1.0,
+    num_joints: int = 2,
+    num_feet: int = 4,
+    num_support: int = 2,
+    support_force: float = 0.0,
+    support_contact_time: float = 0.0,
+    mass: float | None = 26.581,
+    start_x: float = -0.9,
+    command: tuple[float, float, float] = (1.0, 0.0, 0.0),
+    accel_spike_at: int | None = None,
+    accel_spike: float = 0.1,
+) -> Trace:
+    """造一条可控的合成轨迹。
+
+    机器人沿 +x 匀速前进；``accel_spike_at`` 指定在第几帧把速度永久抬高
+    ``accel_spike``，用于制造一次可预期的不稳定计数。
+    """
+    frames = steps + 1
+    time = np.arange(frames, dtype=np.float64) * dt
+
+    speed_profile = np.full(frames, float(speed), dtype=np.float64)
+    if accel_spike_at is not None:
+        speed_profile[accel_spike_at:] += accel_spike
+
+    x = start_x + np.concatenate([[0.0], np.cumsum(speed_profile[1:] * dt)])
+
+    base_pos_w = np.zeros((frames, num_envs, 3), dtype=np.float64)
+    base_pos_w[:, :, 0] = x[:, None]
+
+    base_lin_vel_w = np.zeros((frames, num_envs, 3), dtype=np.float64)
+    base_lin_vel_w[:, :, 0] = speed_profile[:, None]
+
+    projected_gravity_b = np.zeros((frames, num_envs, 3), dtype=np.float64)
+    projected_gravity_b[:, :, 0] = -math.sin(tilt_rad)
+    projected_gravity_b[:, :, 2] = -math.cos(tilt_rad)
+
+    joint_torque = np.full((frames, num_envs, num_joints), float(torque), dtype=np.float64)
+    joint_vel_arr = np.full((frames, num_envs, num_joints), float(joint_vel), dtype=np.float64)
+
+    feet_pos_w = np.zeros((frames, num_envs, num_feet, 2), dtype=np.float64)
+    if num_feet:
+        offsets = np.linspace(-0.3, 0.3, num_feet)
+        for foot_index, offset_x in enumerate(offsets):
+            feet_pos_w[:, :, foot_index, 0] = x[:, None] + offset_x
+            feet_pos_w[:, :, foot_index, 1] = 0.1 if foot_index % 2 else -0.1
+
+    support_force_n = np.full((frames, num_envs, num_support), float(support_force), dtype=np.float64)
+    support_contact_time_s = np.full(
+        (frames, num_envs, num_support), float(support_contact_time), dtype=np.float64
+    )
+
+    return Trace(
+        dt=dt,
+        command=np.asarray(command, dtype=np.float64),
+        base_pos_w=base_pos_w,
+        base_lin_vel_w=base_lin_vel_w,
+        base_lin_vel_b=base_lin_vel_w.copy(),
+        base_ang_vel_b=np.zeros((frames, num_envs, 3), dtype=np.float64),
+        projected_gravity_b=projected_gravity_b,
+        joint_torque=joint_torque,
+        joint_vel=joint_vel_arr,
+        feet_pos_w=feet_pos_w,
+        support_force_n=support_force_n,
+        support_contact_time_s=support_contact_time_s,
+        done=np.zeros((frames, num_envs), dtype=np.float64),
+        mass_kg=mass,
+    )
+
+
+def short_protocol(**overrides) -> EvalProtocol:
+    """时间很短、成功距离很小的协议，便于手算期望值。"""
+    kwargs: dict[str, object] = {"steps": 10}
+    kwargs.update(overrides)
+    return replace(get_protocol("standard_v1"), **kwargs)  # type: ignore[arg-type]
+
+
+# ------------------------------------------------------------------------------- 测试
+
+
+def test_perfect_tracker_succeeds():
+    """匀速前进、姿态水平：成功率 1，跟踪误差 0。"""
+    protocol = short_protocol(success_distance_m=0.09)
+    metrics = compute_metrics(make_trace(), get_profile("solenopsis"), protocol)
+
+    assert metrics["success_rate"] == 1.0, metrics["success_rate"]
+    assert metrics["success_count"] == 3.0
+    assert abs(metrics["mean_lin_vel_error"]) < 1e-9, metrics["mean_lin_vel_error"]
+    assert abs(metrics["mean_yaw_vel_error"]) < 1e-9
+    assert abs(metrics["avg_max_tilt_rad"]) < 1e-9
+    assert metrics["unstability_rate"] == 0.0
+    # speed=1 m/s 从 start_x=-0.9 出发：第 4 帧进度 0.08 < 0.09，第 5 帧 0.10 >= 0.09，
+    # 因此首次越线在第 5 帧，耗时 5*0.02 = 0.10 s。
+    assert abs(metrics["avg_time_consumption_s"] - 0.10) < 1e-9, metrics["avg_time_consumption_s"]
+    return "success_rate=1.00, t=0.10 s, 跟踪误差 0"
+
+
+def test_stationary_robot_fails():
+    """原地不动：成功率 0，最终进度 0。"""
+    protocol = short_protocol(success_distance_m=0.1)
+    metrics = compute_metrics(make_trace(speed=0.0), get_profile("solenopsis"), protocol)
+
+    assert metrics["success_rate"] == 0.0
+    assert abs(metrics["avg_final_progress_m"]) < 1e-9
+    assert math.isnan(metrics["avg_time_consumption_s"]), metrics["avg_time_consumption_s"]
+    assert math.isnan(metrics["time_efficiency"])
+    return "success_rate=0.00, 进度 0, 耗时 nan"
+
+
+def test_faller_is_not_success():
+    """走够了距离但倾角超阈值：不算成功。"""
+    protocol = short_protocol(success_distance_m=0.1, tilt_threshold_rad=0.70)
+    # 倾角 1.0 rad > 0.70 rad
+    metrics = compute_metrics(make_trace(tilt_rad=1.0), get_profile("solenopsis"), protocol)
+
+    assert abs(metrics["avg_final_progress_m"] - 0.2) < 1e-9
+    assert metrics["success_rate"] == 0.0, "倾角超限却算成功了"
+    assert abs(metrics["avg_max_tilt_rad"] - 1.0) < 1e-9
+    assert metrics["avg_stable_time_s"] == 0.0
+    return "进度达成但 success_rate=0.00，max_tilt=1.00 rad"
+
+
+def test_energy_and_cot_match_hand_calculation():
+    """能耗与 COT 与手算值一致。"""
+    steps, dt, joints = 10, 0.02, 2
+    mass = 26.581
+    protocol = short_protocol(steps=steps)
+    trace = make_trace(steps=steps, dt=dt, num_joints=joints, mass=mass)
+    metrics = compute_metrics(trace, get_profile("solenopsis"), protocol)
+
+    expected_energy = 1.0 * 1.0 * joints * steps * dt  # |tau*vel| * 关节数 * 步数 * dt
+    expected_route = 1.0 * steps * dt
+    # 分母带 1e-6 保护项，与旧 eval.py 的 `route + 1e-6` 一致
+    expected_cot = expected_energy / ((expected_route + 1e-6) * mass * G)
+
+    assert abs(metrics["avg_energy_j"] - expected_energy) < 1e-12, (metrics["avg_energy_j"], expected_energy)
+    assert abs(metrics["avg_cost_of_transport"] - expected_cot) < 1e-12, (
+        metrics["avg_cost_of_transport"],
+        expected_cot,
+    )
+    assert abs(metrics["robot_mass_kg"] - mass) < 1e-12
+    return f"energy={expected_energy:.4f} J, COT={expected_cot:.5f}, mass={mass} kg（自动取自 Trace）"
+
+
+def test_signed_energy_convention_differs():
+    """有符号口径在存在负功时给出更小的能耗；两种约定都被记录。"""
+    steps, dt = 10, 0.02
+    trace = make_trace(steps=steps, dt=dt, num_joints=2, joint_vel=1.0)
+    # 让后一半控制步的力矩反向，制造负功：帧 1..5 为 +1，帧 6..10 为 -1。
+    half = steps // 2
+    trace.joint_torque[1 + half :] = -1.0
+
+    absolute = compute_metrics(trace, get_profile("solenopsis"), short_protocol(energy_convention="absolute"))
+    signed = compute_metrics(trace, get_profile("solenopsis"), short_protocol(energy_convention="signed"))
+
+    assert absolute["avg_energy_j"] > signed["avg_energy_j"], (
+        absolute["avg_energy_j"],
+        signed["avg_energy_j"],
+    )
+    assert abs(signed["avg_energy_j"]) < 1e-12, "正负功抵消后应为 0"
+    assert absolute["energy_convention"] == "absolute"
+    assert signed["energy_convention"] == "signed"
+    return f"absolute={absolute['avg_energy_j']:.4f} J, signed={signed['avg_energy_j']:.4f} J"
+
+
+def test_instability_counts_acceleration_spikes():
+    """一次速度跳变恰好计一次不稳定。"""
+    steps, dt = 10, 0.02
+    protocol = replace(
+        short_protocol(steps=steps), acc_threshold_g=2.0 / G
+    )
+    # 第 5 帧速度永久 +0.1 m/s → 该处加速度 0.1/0.02 = 5 m/s² > 2 m/s²
+    metrics = compute_metrics(
+        make_trace(steps=steps, dt=dt, accel_spike_at=5, accel_spike=0.1),
+        get_profile("solenopsis"),
+        protocol,
+    )
+    assert abs(metrics["unstability_rate"] - 1.0 / steps) < 1e-12, metrics["unstability_rate"]
+    return f"unstability_rate={metrics['unstability_rate']:.3f} (= 1/{steps})"
+
+
+def test_cost_of_transport_uses_trace_mass_over_profile():
+    """Trace 里的质量优先于 profile 兜底值。"""
+    profile = replace(get_profile("solenopsis"), mass_kg=999.0)
+    metrics = compute_metrics(make_trace(mass=26.581), profile, short_protocol())
+    assert abs(metrics["robot_mass_kg"] - 26.581) < 1e-12
+
+    trace_no_mass = make_trace(mass=None)
+    metrics_fallback = compute_metrics(trace_no_mass, profile, short_protocol())
+    assert abs(metrics_fallback["robot_mass_kg"] - 999.0) < 1e-12
+    return "Trace 优先，profile 兜底"
+
+
+def test_report_formatters_run_on_real_shapes():
+    """报告层三个格式化函数都要能真跑——它们之前在没有任何测试覆盖的情况下
+    把一个不存在的 numpy 参数留在里面，直到实跑才炸。"""
+    spec = get_terrain_spec("standard_v1")
+    terrain_types = ["stairs_up", "boxes"]
+    level_ids = np.array([0, 0, 1, 1, 0, 0, 1, 1])
+    type_ids = np.array([0, 0, 0, 0, 1, 1, 1, 1])
+    per_env = {
+        "success": [1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0],
+        "max_progress": [3.0, 3.0, 1.0, 1.0, 2.5, 1.5, 3.0, 3.0],
+        "max_tilt": [0.1] * 8,
+    }
+    matrix = build_upper_limit_matrix(per_env, level_ids, type_ids, terrain_types, spec, 2)
+
+    # 普通数值
+    text = format_numpy_matrix(matrix["matrix"]["success_rate"], title="success rate")
+    assert "success rate" in text and "1.00" in text, text
+
+    # 含 nan：把 title 里的 "nan" 排除在外，只检查矩阵主体
+    empty = build_upper_limit_matrix(
+        per_env, level_ids, type_ids, terrain_types, spec, 4
+    )
+    body_only = format_numpy_matrix(empty["matrix"]["success_rate"], title="unassigned levels").split("\n", 1)[1]
+    assert "-" in body_only, f"nan 没有被替换成 -：{body_only}"
+    assert "nan" not in body_only, f"主体里仍出现 nan：{body_only}"
+
+    # 上限汇总：走 build_upper_limit_matrix 的真实输出结构
+    summary = format_upper_limit_summary(matrix)
+    assert "success rate" in summary and "score" in summary, summary
+    assert "difficulty units" in summary
+
+    # 逐等级矩阵（format_matrix）
+    table = format_matrix(matrix["matrix"]["success_rate"], terrain_types, title="table")
+    assert "level" in table and "stairs_up" in table, table
+
+    # 单指标与汇总
+    metrics = compute_metrics(make_trace(), get_profile("solenopsis"), short_protocol(success_distance_m=0.05))
+    assert "success rate" in format_metric_result("access_rate", metrics)
+    assert "protocol" in format_rollout_summary(metrics)
+    return "四个格式化函数在真实结构上都能跑，nan 正确显示为 -"
+
+
+def test_minimum_terrain_size_for_bounds():
+    """地砖下限公式：与网格行数无关，取默认值时是 3.8 m。
+
+    这个函数必须在纯内核里——编排进程不启动仿真，无法 import 任何依赖 isaaclab
+    的模块（会因缺 pxr 失败）。之前它放在适配层，父进程一调用就崩。
+    """
+    protocol = get_protocol("standard_v1")
+    # start_x = -0.9, buffer = 3.0, border = 2.0 → 2*(0.9+3.0-2.0) = 3.8
+    assert abs(protocol.minimum_terrain_size_for_bounds() - 3.8) < 1e-12
+
+    # clean 预设的地砖应当不小于该下限
+    clean = get_protocol("standard_v1_clean")
+    assert clean.terrain_size_m >= clean.minimum_terrain_size_for_bounds()
+
+    # 与网格行数无关
+    for levels in (2, 5, 8, 16):
+        assert abs(replace(protocol, num_levels=levels).minimum_terrain_size_for_bounds() - 3.8) < 1e-12
+
+    # border 与 buffer 可调，且不会返回负数
+    assert abs(protocol.minimum_terrain_size_for_bounds(border_width=1.0, distance_buffer=1.0) - 1.8) < 1e-12
+    assert protocol.minimum_terrain_size_for_bounds(border_width=10.0, distance_buffer=0.0) == 0.0
+
+    # 起始位置越靠外，要求越大
+    far = replace(protocol, start_pose=(-1.5, 0.0, 0.0))
+    assert far.minimum_terrain_size_for_bounds() > protocol.minimum_terrain_size_for_bounds()
+    return "默认 3.8 m，与行数无关，参数可调且非负"
+
+
+def test_missing_morphology_returns_nan_not_zero():
+    """没有足端 / 没有支撑体的机器人应返回 nan，而不是看起来像测出来的 0。"""
+    profile = RobotProfile(
+        name="no_morphology",
+        mass_kg=10.0,
+        feet_body_patterns=(),
+        support_body_patterns=(),
+        actuated_joint_patterns=(".*",),
+    )
+    metrics = compute_metrics(make_trace(num_feet=0, num_support=0), profile, short_protocol())
+    assert math.isnan(metrics["avg_stability_margin_m"]), metrics["avg_stability_margin_m"]
+    assert math.isnan(metrics["avg_support_contact_ratio"])
+    assert math.isnan(metrics["successful_runs_with_support_ratio"])
+    assert metrics["stability_margin_method"] == "unavailable"
+    return "缺失形态 → nan（旧实现给 0.00）"
+
+
+def test_support_contact_events_and_ratio():
+    """支撑接触比例与事件计数可手算。"""
+    steps, dt = 10, 0.02
+    trace = make_trace(steps=steps, dt=dt, support_force=0.0)
+    # 连续 3 帧有超过阈值的支撑接触 → 1 次事件，占 3/10 步
+    trace.support_force_n[2:5] = 100.0
+    trace.support_contact_time_s[2:5] = 0.05
+
+    protocol = replace(short_protocol(steps=steps, success_distance_m=0.1))
+    metrics = compute_metrics(trace, get_profile("solenopsis"), protocol)
+
+    assert abs(metrics["avg_support_contact_ratio"] - 3.0 / steps) < 1e-12, metrics["avg_support_contact_ratio"]
+    assert abs(metrics["avg_support_events"] - 1.0) < 1e-12, metrics["avg_support_events"]
+    assert abs(metrics["successful_runs_with_support_ratio"] - 1.0) < 1e-12
+    return "contact_ratio=0.300, events=1.0, 成功率内用支撑=100%"
+
+
+def test_support_margin_sign_and_tilt_formula():
+    """支撑裕度在包围盒内外符号正确；倾角公式反解一致。"""
+    root_inside = np.array([[0.0, 0.0]])
+    feet = np.array([[[-1.0, -1.0], [1.0, -1.0], [1.0, 1.0], [-1.0, 1.0]]], dtype=np.float64)
+    inside = support_margin_xy(root_inside, feet)
+    assert abs(inside[0] - 1.0) < 1e-12, inside
+
+    root_outside = np.array([[2.0, 0.0]])
+    outside = support_margin_xy(root_outside, feet)
+    assert abs(outside[0] - (-1.0)) < 1e-12, outside
+
+    theta = 0.5
+    gravity_b = np.array([[[-math.sin(theta), 0.0, -math.cos(theta)]]], dtype=np.float64)
+    tilt = tilt_from_projected_gravity(gravity_b)
+    assert abs(tilt[0, 0] - theta) < 1e-9
+    return "盒内 +1.000，盒外 -1.000，倾角反解 0.500 rad"
+
+
+def test_accumulated_and_score_match_legged_gym_formula():
+    """累积成功率与 score 与 legged_gym criterion.py 的公式一致。"""
+    rates = [1.0, 0.8, 0.5, 0.0]
+    accumulated = accumulated_success(rates)
+    assert np.allclose(accumulated, [1.0, 0.8, 0.4, 0.0]), accumulated
+
+    # 手算：i=0 → 1.0*1*(1-0.8)=0.2；i=1 → 0.8*2*(1-0.5)=0.8；
+    #       i=2 → 0.4*3*(1-0.0)=1.2；i=3 → 0.0*4*1=0.0
+    expected = 0.2 + 0.8 + 1.2 + 0.0
+    assert abs(upper_limit_score(rates) - expected) < 1e-12, (upper_limit_score(rates), expected)
+
+    # 全通过与全失败是两个极端
+    assert abs(upper_limit_score([1.0] * 4) - 4.0) < 1e-12
+    assert abs(upper_limit_score([0.0] * 4)) < 1e-12
+    return f"score={expected:.2f}（手算），全通过=4.00，全失败=0.00"
+
+
+def test_upper_limit_matrix_cells():
+    """上限矩阵按「等级 × 地形」正确分格。"""
+    spec = get_terrain_spec("standard_v1")
+    terrain_types = ["stairs_up", "boxes"]
+    num_levels = 2
+    envs_per_cell = 2
+
+    level_ids = np.array([0, 0, 1, 1, 0, 0, 1, 1])
+    type_ids = np.array([0, 0, 0, 0, 1, 1, 1, 1])
+    # stairs_up: 等级0 两个都成功，等级1 两个都失败
+    # boxes:     等级0 一个成功，等级1 两个都成功
+    per_env = {
+        "success": [1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0],
+        "max_progress": [3.0, 3.0, 1.0, 1.0, 2.5, 1.5, 3.0, 3.0],
+        "max_tilt": [0.1] * 8,
+    }
+    result = build_upper_limit_matrix(
+        per_env, level_ids, type_ids, terrain_types, spec, num_levels
+    )
+
+    stairs = result["per_type"]["stairs_up"]
+    boxes = result["per_type"]["boxes"]
+    assert stairs["success_rate"] == [1.0, 0.0], stairs["success_rate"]
+    assert boxes["success_rate"] == [0.5, 1.0], boxes["success_rate"]
+    assert np.allclose(stairs["success_rate_accumulated"], [1.0, 0.0])
+    assert np.allclose(boxes["success_rate_accumulated"], [0.5, 0.5])
+    # 行=等级、列=地形
+    assert result["matrix"]["success_rate"] == [[1.0, 0.5], [0.0, 1.0]]
+    # 难度值取自规格
+    assert result["matrix"]["difficulty"][0] == [0.04, 0.04]
+    assert result["matrix"]["difficulty"][1] == [0.06, 0.06]
+    assert result["num_levels"] == num_levels
+    assert result["scores"]["stairs_up"] == upper_limit_score([1.0, 0.0])
+    return "矩阵 [[1.00,0.50],[0.00,1.00]]，难度 [[0.04,0.04],[0.06,0.06]]"
+
+
+def test_fingerprint_tracks_protocol_changes():
+    """改变口径会改变指纹；改变种子不会。"""
+    base = get_protocol("standard_v1")
+    same = get_protocol("standard_v1")
+    assert base.fingerprint() == same.fingerprint()
+
+    changed_energy = replace(base, energy_convention="signed")
+    changed_tilt = replace(base, tilt_threshold_rad=0.5)
+    pushed = replace(base, enable_interval_pushes=True)
+    reseeded = replace(base, seeds=(1, 2, 3))
+
+    assert base.fingerprint() != changed_energy.fingerprint()
+    assert base.fingerprint() != changed_tilt.fingerprint()
+    assert base.fingerprint() != pushed.fingerprint()
+    assert base.fingerprint() == reseeded.fingerprint(), "种子不影响单次 rollout 的口径"
+    return "能耗约定/倾角阈值/推力开关都改变指纹，种子不改变"
+
+
+def test_trace_validation_rejects_mismatched_shapes():
+    """形状不一致要早失败。"""
+    trace = make_trace(num_envs=2, steps=5, num_joints=3)
+    trace.joint_vel = np.zeros((6, 3, 3))  # 环境数写成 3，应为 2
+    try:
+        trace.validate()
+    except ValueError as exc:
+        assert "joint_vel" in str(exc), str(exc)
+        return "捕获到形状错误"
+    raise AssertionError("形状不一致却没有报错")
+
+
+def test_trace_roundtrip(tmp_path: Path | None = None):
+    """轨迹存盘再读回，指标不变——这是「事后重新分析」的前提。"""
+    import tempfile
+
+    trace = make_trace(steps=8)
+    protocol = short_protocol(steps=8, success_distance_m=0.05)
+    profile = get_profile("solenopsis")
+    before = compute_metrics(trace, profile, protocol)
+
+    with tempfile.TemporaryDirectory() as directory:
+        path = str(Path(directory) / "trace.npz")
+        trace.save(path)
+        reloaded = Trace.load(path)
+        after = compute_metrics(reloaded, profile, protocol)
+
+    for key in before:
+        if key.startswith("_"):
+            continue
+        left, right = before[key], after[key]
+        if isinstance(left, float) and math.isnan(left):
+            assert math.isnan(right), key
+        else:
+            assert left == right, (key, left, right)
+    return f"存盘往返后 {len([k for k in before if not k.startswith('_')])} 个指标完全一致"
+
+
+def test_report_filters_and_formats():
+    """输出层按评估类型裁剪，且 nan 不炸格式串。"""
+    metrics = compute_metrics(make_trace(num_feet=0, num_support=0), get_profile("solenopsis"), short_protocol())
+    selected = filter_metrics(metrics, "stability_margin")
+    assert list(selected.keys()) == ["avg_stability_margin_m"]
+
+    text = format_metric_result("stability_margin", metrics)
+    assert "nan" in text, text
+    text = format_metric_result("access_rate", metrics)
+    assert "success rate" in text
+    return "filter_metrics 裁剪正确，nan 格式化安全"
+
+
+def test_profile_registry():
+    """加机器人只改数据：火蚁有轮与支撑体，四足没有支撑体语义。"""
+    solenopsis = get_profile("solenopsis")
+    assert solenopsis.feet_body_patterns == (".*_wheel",)
+    assert solenopsis.support_body_patterns == ("base_link", ".*_knee")
+
+    lite3 = get_profile("lite3")
+    assert lite3.support_body_patterns == ()
+
+    try:
+        get_profile("nonexistent")
+    except KeyError:
+        return "solenopsis / lite3 描述可用，未知名字报错"
+    raise AssertionError("未知机器人没有报错")
+
+
+def test_naming_resolves_fire_ant_bodies():
+    """火蚁的 link/joint 名字能被描述里的正则解析出来。"""
+    body_names = [
+        "base_link",
+        "fl_knee", "fr_knee", "hl_knee", "hr_knee",
+        "fl_wheel", "fr_wheel", "hl_wheel", "hr_wheel",
+    ]
+    resolution = resolve_matching_indices(get_profile("solenopsis").feet_body_patterns, body_names)
+    assert resolution.names == ["fl_wheel", "fr_wheel", "hl_wheel", "hr_wheel"], resolution.names
+    assert resolution.indices == [5, 6, 7, 8]
+    assert not resolution.unmatched_patterns
+
+    support = resolve_matching_indices(get_profile("solenopsis").support_body_patterns, body_names)
+    assert support.names == ["base_link", "fl_knee", "fr_knee", "hl_knee", "hr_knee"], support.names
+
+    joint_names = [
+        "fl_knee_joint", "fr_knee_joint", "hl_knee_joint", "hr_knee_joint",
+        "fl_wheel_joint", "fr_wheel_joint", "hl_wheel_joint", "hr_wheel_joint",
+    ]
+    joints = resolve_matching_indices(get_profile("solenopsis").actuated_joint_patterns, joint_names)
+    assert len(joints) == 8, joints.names
+    return f"足端 4 个、支撑体 5 个、驱动关节 {len(joints)} 个"
+
+
+def test_naming_does_not_crash_when_morphology_is_missing():
+    """换一个没有轮子的机器人：未命中的正则被记录下来，而不是抛异常。"""
+    quadruped_bodies = ["base", "FL_hip", "FL_thigh", "FL_shank", "FL_FOOT"]
+    resolution = resolve_matching_indices(get_profile("solenopsis").feet_body_patterns, quadruped_bodies)
+    assert len(resolution) == 0
+    assert resolution.unmatched_patterns == [".*_wheel"]
+    assert not resolution
+
+    # 四足自己的描述能解析
+    lite3 = resolve_matching_indices(get_profile("lite3").feet_body_patterns, quadruped_bodies)
+    assert lite3.names == ["FL_FOOT"]
+    # 空描述合法，得到空结果
+    empty = resolve_matching_indices(get_profile("lite3").support_body_patterns, quadruped_bodies)
+    assert len(empty) == 0 and not empty.unmatched_patterns
+    return "无轮机器人：未命中被记录，不崩溃"
+
+
+def test_naming_reports_ambiguous_and_bad_regex():
+    """一个名字命中多个正则会被记录；正则写错要立刻报错。"""
+    resolution = resolve_matching_indices((".*_FOOT", "FL_.*"), ["FL_FOOT", "FR_FOOT"])
+    assert resolution.names == ["FL_FOOT", "FR_FOOT"]
+    assert resolution.ambiguous_names == [("FL_FOOT", [".*_FOOT", "FL_.*"])], resolution.ambiguous_names
+
+    try:
+        resolve_matching_indices(("[unclosed",), ["anything"])
+    except ValueError as exc:
+        assert "无法编译" in str(exc)
+        return "多重命中被记录，坏正则报错"
+    raise AssertionError("坏正则没有报错")
+
+
+def test_aggregate_runs_mean_std_and_nan_handling():
+    """多次试验聚合：mean/std 正确，nan 不污染结果，非数值字段单独记录。"""
+    runs = [
+        (0, {"success_rate": 0.5, "avg_energy_j": 100.0, "profile": "solenopsis", "avg_cost_of_transport": float("nan")}),
+        (1, {"success_rate": 1.0, "avg_energy_j": 200.0, "profile": "solenopsis", "avg_cost_of_transport": 0.4}),
+        (2, {"success_rate": 0.75, "avg_energy_j": 150.0, "profile": "solenopsis", "avg_cost_of_transport": float("nan")}),
+    ]
+    aggregate = aggregate_runs(runs)
+
+    assert aggregate["num_runs"] == 3
+    assert aggregate["seeds"] == [0, 1, 2]
+
+    success = aggregate["summary"]["success_rate"]
+    assert abs(success["mean"] - 0.75) < 1e-12, success
+    assert abs(success["std"] - 0.25) < 1e-12, success
+    assert success["min"] == 0.5 and success["max"] == 1.0
+    assert success["values"] == [0.5, 1.0, 0.75]
+
+    # nan 被剔除，只剩 1 个有效值 → std 记 0
+    cot = aggregate["summary"]["avg_cost_of_transport"]
+    assert abs(cot["mean"] - 0.4) < 1e-12, cot
+    assert cot["std"] == 0.0
+    assert cot["finite_runs"] == 1
+
+    # 非数值字段单独放在 constant 里
+    assert aggregate["constant"]["profile"] == "solenopsis"
+    assert "profile_INCONSISTENT" not in aggregate["constant"]
+
+    # 全部无效 → mean 是 nan，且记录了有效次数
+    all_nan = aggregate_runs([(0, {"x": float("nan")}), (1, {"x": float("nan")})])
+    assert math.isnan(all_nan["summary"]["x"]["mean"])
+    assert all_nan["summary"]["x"]["finite_runs"] == 0
+
+    text = format_aggregate_summary(aggregate)
+    assert "success_rate" in text and "±" in text
+    return "mean/std 正确，nan 被剔除，非数值字段单独登记"
+
+
+def test_aggregate_runs_flags_inconsistent_metadata():
+    """两次 run 的口径不一致时要显式暴露，而不是悄悄取第一个。"""
+    aggregate = aggregate_runs(
+        [
+            (0, {"success_rate": 1.0, "energy_convention": "absolute"}),
+            (1, {"success_rate": 1.0, "energy_convention": "signed"}),
+        ]
+    )
+    assert "energy_convention_INCONSISTENT" in aggregate["constant"], aggregate["constant"]
+    return "口径不一致会显式标记"
+
+
+# ------------------------------------------------------------------------------- 运行器
+
+
+def main() -> int:
+    tests = [value for name, value in sorted(globals().items()) if name.startswith("test_")]
+    failures: list[tuple[str, str]] = []
+    for test in tests:
+        name = test.__name__
+        try:
+            note = test()
+        except Exception as exc:  # noqa: BLE001 - 测试运行器需要捕获全部异常
+            failures.append((name, f"{type(exc).__name__}: {exc}"))
+            print(f"FAIL  {name}\n        {type(exc).__name__}: {exc}")
+        else:
+            suffix = f"  —  {note}" if note else ""
+            print(f"ok    {name}{suffix}")
+
+    print()
+    if failures:
+        print(f"{len(failures)}/{len(tests)} 个测试失败")
+        return 1
+    print(f"全部 {len(tests)} 个测试通过")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..02815af
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,85 @@
+[build-system]
+requires = ["setuptools", "toml"]
+build-backend = "setuptools.build_meta"
+
+[tool.isort]
+
+atomic = true
+profile = "black"
+line_length = 120
+py_version = 310
+skip_glob = ["docs/*", "logs/*"]
+group_by_package = true
+
+sections = [
+    "FUTURE",
+    "STDLIB",
+    "THIRDPARTY",
+    "ISAACLABPARTY",
+    "FIRSTPARTY",
+    "LOCALFOLDER",
+]
+extra_standard_library = [
+    "numpy",
+    "h5py",
+    "open3d",
+    "torch",
+    "tensordict",
+    "bpy",
+    "matplotlib",
+    "gymnasium",
+    "gym",
+    "scipy",
+    "hid",
+    "yaml",
+    "prettytable",
+    "toml",
+    "trimesh",
+    "tqdm",
+    "psutil",
+]
+known_thirdparty = [
+    "isaacsim.core.api",
+    "omni.replicator.core",
+    "pxr",
+    "omni.kit.*",
+    "warp",
+    "carb",
+    "Semantics",
+]
+known_isaaclabparty = [
+    "isaaclab",
+    "isaaclab_tasks",
+    "isaaclab_assets",
+    "isaaclab_mimic",
+    "isaaclab_rl"
+]
+
+# Modify the following to include the package names of your first-party code
+known_firstparty = "rl_training"
+known_local_folder = "config"
+
+[tool.pyright]
+
+exclude = [
+    "**/__pycache__",
+    "**/docs",
+    "**/logs",
+    ".git",
+    ".vscode",
+]
+
+typeCheckingMode = "basic"
+pythonVersion = "3.11"
+pythonPlatform = "Linux"
+enableTypeIgnoreComments = true
+
+# This is required as the CI pre-commit does not download the module (i.e. numpy, torch, prettytable)
+# Therefore, we have to ignore missing imports
+reportMissingImports = "none"
+# This is required to ignore for type checks of modules with stubs missing.
+reportMissingModuleSource = "none" # -> most common: prettytable in mdp managers
+
+reportGeneralTypeIssues = "none"       # -> raises 218 errors (usage of literal MISSING in dataclasses)
+reportOptionalMemberAccess = "warning" # -> raises 8 errors
+reportPrivateUsage = "warning"
diff --git a/scripts/reinforcement_learning/rl_utils.py b/scripts/reinforcement_learning/rl_utils.py
new file mode 100644
index 0000000..d344b3c
--- /dev/null
+++ b/scripts/reinforcement_learning/rl_utils.py
@@ -0,0 +1,44 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+
+import isaaclab.utils.math as math_utils
+
+
+def camera_follow(env):
+    if not hasattr(camera_follow, "smooth_camera_positions"):
+        camera_follow.smooth_camera_positions = []
+    robot_pos = env.unwrapped.scene["robot"].data.root_pos_w[0]
+    robot_quat = env.unwrapped.scene["robot"].data.root_quat_w[0]
+    camera_offset = torch.tensor([-3.0, 0.0, 0.5], dtype=torch.float32, device=env.device)
+    camera_pos = math_utils.transform_points(
+        camera_offset.unsqueeze(0), pos=robot_pos.unsqueeze(0), quat=robot_quat.unsqueeze(0)
+    ).squeeze(0)
+    # camera_pos[2] = torch.clamp(camera_pos[2], min=0.1)
+    window_size = 50
+    camera_follow.smooth_camera_positions.append(camera_pos)
+    if len(camera_follow.smooth_camera_positions) > window_size:
+        camera_follow.smooth_camera_positions.pop(0)
+    smooth_camera_pos = torch.mean(torch.stack(camera_follow.smooth_camera_positions), dim=0)
+    env.unwrapped.viewport_camera_controller.set_view_env_index(env_index=0)
+    env.unwrapped.viewport_camera_controller.update_view_location(
+        eye=smooth_camera_pos.cpu().numpy(), lookat=robot_pos.cpu().numpy()
+    )
+
+
+
+
+import os
+import pickle
+from typing import Any
+
+def reindex_feet(vec):
+    return vec[:, [1, 0, 3, 2]]
+
+def reindex(vec):
+    return vec[:, [3, 4, 5, 0, 1, 2, 9, 10, 11, 6, 7, 8]]
+
diff --git a/scripts/reinforcement_learning/rsl_rl/cli_args.py b/scripts/reinforcement_learning/rsl_rl/cli_args.py
new file mode 100644
index 0000000..717037f
--- /dev/null
+++ b/scripts/reinforcement_learning/rsl_rl/cli_args.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import argparse
+import random
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg
+
+
+def add_rsl_rl_args(parser: argparse.ArgumentParser):
+    """Add RSL-RL arguments to the parser.
+
+    Args:
+        parser: The parser to add the arguments to.
+    """
+    # create a new argument group
+    arg_group = parser.add_argument_group("rsl_rl", description="Arguments for RSL-RL agent.")
+    # -- experiment arguments
+    arg_group.add_argument(
+        "--experiment_name", type=str, default=None, help="Name of the experiment folder where logs will be stored."
+    )
+    arg_group.add_argument("--run_name", type=str, default=None, help="Run name suffix to the log directory.")
+    # -- load arguments
+    arg_group.add_argument("--resume", action="store_true", default=False, help="Whether to resume from a checkpoint.")
+    arg_group.add_argument("--load_run", type=str, default=None, help="Name of the run folder to resume from.")
+    arg_group.add_argument("--checkpoint", type=str, default=None, help="Checkpoint file to resume from.")
+    # -- logger arguments
+    arg_group.add_argument(
+        "--logger", type=str, default=None, choices={"wandb", "tensorboard", "neptune"}, help="Logger module to use."
+    )
+    arg_group.add_argument(
+        "--log_project_name", type=str, default=None, help="Name of the logging project when using wandb or neptune."
+    )
+
+
+def parse_rsl_rl_cfg(task_name: str, args_cli: argparse.Namespace) -> RslRlOnPolicyRunnerCfg:
+    """Parse configuration for RSL-RL agent based on inputs.
+
+    Args:
+        task_name: The name of the environment.
+        args_cli: The command line arguments.
+
+    Returns:
+        The parsed configuration for RSL-RL agent based on inputs.
+    """
+    from isaaclab_tasks.utils.parse_cfg import load_cfg_from_registry
+
+    # load the default configuration
+    rslrl_cfg: RslRlOnPolicyRunnerCfg = load_cfg_from_registry(task_name, "rsl_rl_cfg_entry_point")
+    rslrl_cfg = update_rsl_rl_cfg(rslrl_cfg, args_cli)
+    return rslrl_cfg
+
+
+def update_rsl_rl_cfg(agent_cfg: RslRlOnPolicyRunnerCfg, args_cli: argparse.Namespace):
+    """Update configuration for RSL-RL agent based on inputs.
+
+    Args:
+        agent_cfg: The configuration for RSL-RL agent.
+        args_cli: The command line arguments.
+
+    Returns:
+        The updated configuration for RSL-RL agent based on inputs.
+    """
+    # override the default configuration with CLI arguments
+    if hasattr(args_cli, "seed") and args_cli.seed is not None:
+        # randomly sample a seed if seed = -1
+        if args_cli.seed == -1:
+            args_cli.seed = random.randint(0, 10000)
+        agent_cfg.seed = args_cli.seed
+    if args_cli.resume is not None:
+        agent_cfg.resume = args_cli.resume
+    if args_cli.load_run is not None:
+        agent_cfg.load_run = args_cli.load_run
+    if args_cli.checkpoint is not None:
+        agent_cfg.load_checkpoint = args_cli.checkpoint
+    if args_cli.run_name is not None:
+        agent_cfg.run_name = args_cli.run_name
+    if args_cli.logger is not None:
+        agent_cfg.logger = args_cli.logger
+    # set the project name for wandb and neptune
+    if agent_cfg.logger in {"wandb", "neptune"} and args_cli.log_project_name:
+        agent_cfg.wandb_project = args_cli.log_project_name
+        agent_cfg.neptune_project = args_cli.log_project_name
+
+    return agent_cfg
diff --git a/scripts/reinforcement_learning/rsl_rl/eval.py b/scripts/reinforcement_learning/rsl_rl/eval.py
new file mode 100644
index 0000000..b45a2df
--- /dev/null
+++ b/scripts/reinforcement_learning/rsl_rl/eval.py
@@ -0,0 +1,716 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""Evaluate an RSL-RL checkpoint with criterion metrics migrated from legged_gym."""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import json
+import os
+import sys
+from datetime import datetime
+
+import numpy as np
+
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+PROJECT_SOURCE_PATH = os.path.join(PROJECT_ROOT, "source", "rl_training")
+if PROJECT_SOURCE_PATH not in sys.path:
+    sys.path.insert(0, PROJECT_SOURCE_PATH)
+
+from isaaclab.app import AppLauncher
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+import cli_args
+
+parser = argparse.ArgumentParser(description="Evaluate an RSL-RL checkpoint with criterion metrics.")
+parser.add_argument("--num_envs", type=int, default=250, help="Number of parallel environments.")
+parser.add_argument("--task", type=str, default="Rough-Deeprobotics-M20-v0", help="Name of the task.")
+parser.add_argument(
+    "--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
+)
+parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment.")
+parser.add_argument(
+    "--eval_type",
+    type=str,
+    default="all",
+    choices=[
+        "all",
+        "upper_limits",
+        "velocity_error",
+        "access_rate",
+        "time_consumption",
+        "energy_consumption",
+        "motion_stability",
+        "stability_margin",
+        "robustness",
+    ],
+)
+parser.add_argument("--episode_length_s", type=float, default=10.0)
+parser.add_argument("--command_x", type=float, default=1.0)
+parser.add_argument("--command_y", type=float, default=0.0)
+parser.add_argument("--command_yaw", type=float, default=0.0)
+parser.add_argument("--terrain_size", type=float, default=2.0, help="Evaluation terrain tile size in meters.")
+parser.add_argument("--success_distance", type=float, default=2.0)
+parser.add_argument("--start_x", type=float, default=-0.9, help="Evaluation reset x position on the terrain tile.")
+parser.add_argument("--start_y", type=float, default=0.0, help="Evaluation reset y position on the terrain tile.")
+parser.add_argument("--num_levels", type=int, default=8, help="Number of difficulty levels for upper_limits.")
+parser.add_argument("--envs_per_cell", type=int, default=8, help="Number of robots per terrain/difficulty cell.")
+parser.add_argument(
+    "--terrain_type",
+    type=str,
+    default="all",
+    choices=["all", "stairs_up", "stairs_down", "smooth_slope", "rough_slope", "boxes"],
+    help="Terrain type used by upper_limits.",
+)
+parser.add_argument("--tilt_threshold", type=float, default=0.70, help="Tilt threshold in radians for success/stability.")
+parser.add_argument("--acc_threshold", type=float, default=2.0, help="Base acceleration threshold for instability.")
+parser.add_argument("--robot_mass", type=float, default=25.482, help="Robot mass used for COT.")
+parser.add_argument("--output", type=str, default=None, help="Optional output JSON path.")
+parser.add_argument("--print_json", action="store_true", default=False, help="Print the saved JSON payload to stdout.")
+parser.add_argument("--verbose", action="store_true", default=False, help="Print per-terrain per-level debug lines.")
+parser.add_argument(
+    "--include_summary",
+    action="store_true",
+    default=False,
+    help="When eval_type=all, also run a second mixed-terrain rollout for summary metrics.",
+)
+cli_args.add_rsl_rl_args(parser)
+AppLauncher.add_app_launcher_args(parser)
+args_cli, hydra_args = parser.parse_known_args()
+
+sys.argv = [sys.argv[0]] + hydra_args
+
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+import gymnasium as gym
+import torch
+from rsl_rl.runners import OnPolicyRunner
+
+from isaaclab.envs import (
+    DirectMARLEnv,
+    DirectMARLEnvCfg,
+    DirectRLEnvCfg,
+    ManagerBasedRLEnvCfg,
+    multi_agent_to_single_agent,
+)
+from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG
+from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper
+from isaaclab_tasks.utils import get_checkpoint_path
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+import rl_training
+import rl_training.tasks  # noqa: F401
+
+EVAL_TYPES = [
+    "upper_limits",
+    "velocity_error",
+    "access_rate",
+    "time_consumption",
+    "energy_consumption",
+    "motion_stability",
+    "stability_margin",
+    "robustness",
+]
+
+TERRAIN_LEVELS = {
+    "stairs_up": [0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18],
+    "stairs_down": [0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18],
+    "smooth_slope": [0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40],
+    "rough_slope": [0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40],
+    "boxes": [0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18],
+}
+
+TERRAIN_TO_SUBTERRAIN = {
+    "stairs_up": "pyramid_stairs",
+    "stairs_down": "pyramid_stairs_inv",
+    "smooth_slope": "hf_pyramid_slope",
+    "rough_slope": "random_rough",
+    "boxes": "boxes",
+}
+
+
+def apply_eval_terrain_baseline(env_cfg) -> None:
+    """Use the same evaluation terrain generator for every task."""
+    if env_cfg.scene.terrain.terrain_type == "plane":
+        env_cfg.scene.terrain.terrain_type = "generator"
+    env_cfg.scene.terrain.terrain_generator = copy.deepcopy(ROUGH_TERRAINS_CFG)
+    env_cfg.scene.terrain.max_init_terrain_level = None
+    env_cfg.scene.terrain.terrain_generator.size = (args_cli.terrain_size, args_cli.terrain_size)
+    env_cfg.scene.terrain.terrain_generator.border_width = 2.0
+    env_cfg.scene.terrain.terrain_generator.curriculum = False
+    env_cfg.scene.terrain.terrain_generator.num_rows = 5
+    env_cfg.scene.terrain.terrain_generator.num_cols = 5
+    env_cfg.scene.terrain.terrain_generator.use_cache = False
+    for sub_cfg in env_cfg.scene.terrain.terrain_generator.sub_terrains.values():
+        if hasattr(sub_cfg, "platform_width"):
+            sub_cfg.platform_width = min(0.4, args_cli.terrain_size * 0.2)
+        if hasattr(sub_cfg, "border_width"):
+            sub_cfg.border_width = min(0.1, args_cli.terrain_size * 0.05)
+        if hasattr(sub_cfg, "step_width"):
+            sub_cfg.step_width = min(0.25, args_cli.terrain_size * 0.125)
+        if hasattr(sub_cfg, "grid_width"):
+            sub_cfg.grid_width = min(0.3, args_cli.terrain_size * 0.15)
+
+
+def resolve_checkpoint(log_root_path: str, load_run: str | None, checkpoint: str | None) -> str:
+    if checkpoint is not None:
+        checkpoint_path = os.path.expanduser(checkpoint)
+        if os.path.isabs(checkpoint_path) or os.path.dirname(checkpoint_path):
+            checkpoint_path = os.path.abspath(checkpoint_path)
+            if not os.path.isfile(checkpoint_path):
+                raise FileNotFoundError(f"Unable to find checkpoint: {checkpoint_path}")
+            return checkpoint_path
+
+    if load_run is not None:
+        run_path = os.path.expanduser(load_run)
+        if os.path.isabs(run_path) or os.path.dirname(run_path):
+            run_path = os.path.abspath(run_path)
+            if not os.path.isdir(run_path):
+                raise FileNotFoundError(f"Unable to find run directory: {run_path}")
+            return get_checkpoint_path(os.path.dirname(run_path), os.path.basename(run_path), checkpoint)
+
+    return get_checkpoint_path(log_root_path, load_run, checkpoint)
+
+
+def set_fixed_command(env_cfg, command_x: float, command_y: float, command_yaw: float, episode_length_s: float) -> None:
+    command_cfg = env_cfg.commands.base_velocity
+    command_cfg.heading_command = False
+    command_cfg.resampling_time_range = (episode_length_s + 0.01, episode_length_s + 0.01)
+    command_cfg.ranges.lin_vel_x = (command_x, command_x)
+    command_cfg.ranges.lin_vel_y = (command_y, command_y)
+    command_cfg.ranges.ang_vel_z = (command_yaw, command_yaw)
+
+
+def configure_eval_env(env_cfg, eval_type: str) -> None:
+    env_cfg.scene.num_envs = args_cli.num_envs
+    env_cfg.episode_length_s = args_cli.episode_length_s
+    env_cfg.seed = args_cli.seed if args_cli.seed is not None else 0
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
+    env_cfg.observations.policy.enable_corruption = False
+    set_fixed_command(env_cfg, args_cli.command_x, args_cli.command_y, args_cli.command_yaw, args_cli.episode_length_s)
+
+    apply_eval_terrain_baseline(env_cfg)
+
+    env_cfg.curriculum.terrain_levels = None
+    env_cfg.curriculum.command_levels = None
+
+    for name in (
+        "randomize_rigid_body_material",
+        "randomize_wheel_material",
+        "randomize_support_material",
+        "randomize_rigid_body_mass",
+        "randomize_rigid_body_mass_base",
+        "randomize_rigid_body_inertia",
+        "randomize_com_positions",
+        "randomize_apply_external_force_torque",
+        "randomize_actuator_gains",
+    ):
+        if hasattr(env_cfg.events, name):
+            setattr(env_cfg.events, name, None)
+
+    if hasattr(env_cfg.events, "randomize_reset_base") and env_cfg.events.randomize_reset_base is not None:
+        env_cfg.events.randomize_reset_base.params = {
+            "pose_range": {
+                "x": (args_cli.start_x, args_cli.start_x),
+                "y": (args_cli.start_y, args_cli.start_y),
+                "z": (0.0, 0.0),
+                "roll": (0.0, 0.0),
+                "pitch": (0.0, 0.0),
+                "yaw": (0.0, 0.0),
+            },
+            "velocity_range": {
+                "x": (0.0, 0.0),
+                "y": (0.0, 0.0),
+                "z": (0.0, 0.0),
+                "roll": (0.0, 0.0),
+                "pitch": (0.0, 0.0),
+                "yaw": (0.0, 0.0),
+            },
+        }
+
+    if hasattr(env_cfg.events, "randomize_push_robot"):
+        env_cfg.events.randomize_push_robot = None
+    if eval_type == "robustness" and hasattr(env_cfg.events, "randomize_push_robot"):
+        # Keep robustness deterministic enough to compare runs, but enable interval pushes.
+        from isaaclab.managers import EventTermCfg as EventTerm
+        import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+
+        env_cfg.events.randomize_push_robot = EventTerm(
+            func=mdp.push_by_setting_velocity,
+            mode="interval",
+            interval_range_s=(2.0, 2.01),
+            params={"velocity_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5)}},
+        )
+
+
+def configure_upper_limit_env(env_cfg, terrain_types: list[str]) -> None:
+    """Configure one fixed terrain grid: rows are difficulty levels, columns are terrain types."""
+    configure_eval_env(env_cfg, "all")
+    terrain_generator = env_cfg.scene.terrain.terrain_generator
+    terrain_generator.curriculum = True
+    terrain_generator.num_rows = args_cli.num_levels
+    terrain_generator.num_cols = len(terrain_types)
+    terrain_generator.difficulty_range = (0.0, 1.0)
+    if hasattr(terrain_generator, "seed"):
+        terrain_generator.seed = args_cli.seed if args_cli.seed is not None else 0
+    env_cfg.scene.terrain.max_init_terrain_level = args_cli.num_levels - 1
+    env_cfg.scene.num_envs = args_cli.num_levels * len(terrain_types) * args_cli.envs_per_cell
+
+    selected_subterrains = [TERRAIN_TO_SUBTERRAIN[terrain_type] for terrain_type in terrain_types]
+    original_subterrains = terrain_generator.sub_terrains
+    terrain_generator.sub_terrains = {
+        subterrain_name: copy.deepcopy(original_subterrains[subterrain_name]) for subterrain_name in selected_subterrains
+    }
+    for name, sub_cfg in terrain_generator.sub_terrains.items():
+        sub_cfg.proportion = selected_subterrains.count(name) / len(selected_subterrains)
+
+    for terrain_type in terrain_types:
+        sub_cfg = terrain_generator.sub_terrains[TERRAIN_TO_SUBTERRAIN[terrain_type]]
+        levels = TERRAIN_LEVELS[terrain_type][: args_cli.num_levels]
+        if terrain_type in ("stairs_up", "stairs_down"):
+            sub_cfg.step_height_range = (levels[0], levels[-1])
+        elif terrain_type == "smooth_slope":
+            sub_cfg.slope_range = (levels[0], levels[-1])
+        elif terrain_type == "rough_slope":
+            sub_cfg.noise_range = (levels[0], levels[-1])
+            sub_cfg.noise_step = 0.01
+        elif terrain_type == "boxes":
+            sub_cfg.grid_height_range = (levels[0], levels[-1])
+
+
+def assign_upper_limit_origins(env, terrain_types: list[str]) -> tuple[torch.Tensor, torch.Tensor]:
+    """Assign an equal number of robots to every terrain/difficulty cell."""
+    base_env = env.unwrapped
+    terrain = base_env.scene.terrain
+    device = base_env.device
+    num_levels = args_cli.num_levels
+    num_types = len(terrain_types)
+    levels = []
+    types = []
+    for terrain_type_id in range(num_types):
+        for level_id in range(num_levels):
+            levels.extend([level_id] * args_cli.envs_per_cell)
+            types.extend([terrain_type_id] * args_cli.envs_per_cell)
+    levels_tensor = torch.tensor(levels, dtype=torch.long, device=device)
+    types_tensor = torch.tensor(types, dtype=torch.long, device=device)
+    terrain.terrain_levels = levels_tensor
+    terrain.terrain_types = types_tensor
+    terrain.env_origins = terrain.terrain_origins[levels_tensor, types_tensor]
+    env.reset()
+    return levels_tensor, types_tensor
+
+
+def tilt_from_projected_gravity(projected_gravity_b: torch.Tensor) -> torch.Tensor:
+    z_axis = torch.clamp(-projected_gravity_b[:, 2], min=-1.0, max=1.0)
+    return torch.acos(z_axis)
+
+
+def support_margin_xy(root_xy: torch.Tensor, foot_xy: torch.Tensor) -> torch.Tensor:
+    min_xy = torch.min(foot_xy, dim=1).values
+    max_xy = torch.max(foot_xy, dim=1).values
+    inside_x = (root_xy[:, 0] >= min_xy[:, 0]) & (root_xy[:, 0] <= max_xy[:, 0])
+    inside_y = (root_xy[:, 1] >= min_xy[:, 1]) & (root_xy[:, 1] <= max_xy[:, 1])
+    inside = inside_x & inside_y
+    inside_dist = torch.minimum(
+        torch.minimum(root_xy[:, 0] - min_xy[:, 0], max_xy[:, 0] - root_xy[:, 0]),
+        torch.minimum(root_xy[:, 1] - min_xy[:, 1], max_xy[:, 1] - root_xy[:, 1]),
+    )
+    outside_dx = torch.clamp(torch.maximum(min_xy[:, 0] - root_xy[:, 0], root_xy[:, 0] - max_xy[:, 0]), min=0.0)
+    outside_dy = torch.clamp(torch.maximum(min_xy[:, 1] - root_xy[:, 1], root_xy[:, 1] - max_xy[:, 1]), min=0.0)
+    outside_dist = torch.sqrt(outside_dx**2 + outside_dy**2)
+    return torch.where(inside, inside_dist, -outside_dist)
+
+
+def run_rollout(env, policy, include_per_env: bool = False) -> dict[str, object]:
+    base_env = env.unwrapped
+    robot = base_env.scene["robot"]
+    dt = base_env.step_dt
+    device = base_env.device
+    max_steps = int(base_env.max_episode_length)
+    command = torch.tensor([args_cli.command_x, args_cli.command_y, args_cli.command_yaw], device=device)
+    command_xy = command[:2].unsqueeze(0)
+    command_yaw = command[2]
+    command_dir = torch.tensor([args_cli.command_x, args_cli.command_y], device=device)
+    if torch.linalg.norm(command_dir) < 1e-6:
+        command_dir = torch.tensor([1.0, 0.0], device=device)
+    command_dir = command_dir / torch.linalg.norm(command_dir)
+
+    obs = env.get_observations()
+    start_pos = robot.data.root_pos_w[:, :3].clone()
+    last_lin_vel = robot.data.root_lin_vel_w[:, :3].clone()
+    complete_time = torch.full((base_env.num_envs,), float("nan"), device=device)
+    energy = torch.zeros(base_env.num_envs, device=device)
+    route = torch.zeros(base_env.num_envs, device=device)
+    lin_error_sum = torch.zeros(base_env.num_envs, device=device)
+    yaw_error_sum = torch.zeros(base_env.num_envs, device=device)
+    instability_count = torch.zeros(base_env.num_envs, device=device)
+    max_tilt = torch.zeros(base_env.num_envs, device=device)
+    stable_time = torch.zeros(base_env.num_envs, device=device)
+    margin_sum = torch.zeros(base_env.num_envs, device=device)
+    margin_count = 0
+    max_progress = torch.zeros(base_env.num_envs, device=device)
+    reached = torch.zeros(base_env.num_envs, dtype=torch.bool, device=device)
+    ever_fallen = torch.zeros(base_env.num_envs, dtype=torch.bool, device=device)
+    support_steps = torch.zeros(base_env.num_envs, device=device)
+    support_events = torch.zeros(base_env.num_envs, device=device)
+    max_support_time = torch.zeros(base_env.num_envs, device=device)
+    previous_support_active = torch.zeros(base_env.num_envs, dtype=torch.bool, device=device)
+
+    wheel_body_ids, _ = robot.find_bodies(".*_wheel")
+    contact_sensor = base_env.scene.sensors["contact_forces"]
+    support_body_ids, _ = contact_sensor.find_bodies(["base_link", ".*_knee"])
+
+    for step in range(max_steps):
+        pre_progress = torch.sum((robot.data.root_pos_w[:, :2] - start_pos[:, :2]) * command_dir.unsqueeze(0), dim=1)
+        max_progress = torch.maximum(max_progress, pre_progress)
+        reached |= pre_progress >= args_cli.success_distance
+
+        with torch.inference_mode():
+            actions = policy(obs)
+            if isinstance(actions, tuple):
+                actions = actions[0]
+            obs, _, dones, _ = env.step(actions)
+
+        lin_vel_b = robot.data.root_lin_vel_b[:, :2]
+        yaw_vel_b = robot.data.root_ang_vel_b[:, 2]
+        lin_error_sum += torch.linalg.norm(lin_vel_b - command_xy, dim=1)
+        yaw_error_sum += torch.abs(yaw_vel_b - command_yaw)
+
+        joint_power = torch.abs(robot.data.applied_torque * robot.data.joint_vel)
+        energy += torch.sum(joint_power, dim=1) * dt
+        route += torch.linalg.norm(robot.data.root_lin_vel_w[:, :2], dim=1) * dt
+
+        acc = torch.linalg.norm(robot.data.root_lin_vel_w[:, :3] - last_lin_vel, dim=1) / dt
+        instability_count += (acc > args_cli.acc_threshold).float()
+        last_lin_vel = robot.data.root_lin_vel_w[:, :3].clone()
+
+        tilt = tilt_from_projected_gravity(robot.data.projected_gravity_b)
+        max_tilt = torch.maximum(max_tilt, tilt)
+        stable_time += (tilt < args_cli.tilt_threshold).float() * dt
+
+        progress = torch.sum((robot.data.root_pos_w[:, :2] - start_pos[:, :2]) * command_dir.unsqueeze(0), dim=1)
+        max_progress = torch.maximum(max_progress, progress)
+        reached |= progress >= args_cli.success_distance
+        newly_success = torch.isnan(complete_time) & (progress >= args_cli.success_distance)
+        complete_time[newly_success] = (step + 1) * dt
+        ever_fallen |= tilt > args_cli.tilt_threshold
+
+        support_force = torch.linalg.norm(
+            contact_sensor.data.net_forces_w[:, support_body_ids, :], dim=-1
+        )
+        support_time = contact_sensor.data.current_contact_time[:, support_body_ids]
+        support_active = torch.any(support_force > 15.0, dim=1)
+        support_steps += support_active.float()
+        support_events += (support_active & ~previous_support_active).float()
+        max_support_time = torch.maximum(max_support_time, torch.amax(support_time, dim=1))
+        previous_support_active = support_active
+
+        if len(wheel_body_ids) >= 3:
+            foot_xy = robot.data.body_pos_w[:, wheel_body_ids, :2]
+            margin_sum += support_margin_xy(robot.data.root_pos_w[:, :2], foot_xy)
+            margin_count += 1
+
+    tilt = tilt_from_projected_gravity(robot.data.projected_gravity_b)
+    success = reached & ~ever_fallen
+    success_count = torch.sum(success).item()
+    success_den = max(base_env.num_envs, 1)
+    successful_time = complete_time[success & torch.isfinite(complete_time)]
+    avg_time = torch.mean(successful_time).item() if successful_time.numel() > 0 else float("nan")
+
+    cot = energy / ((route + 1e-6) * args_cli.robot_mass * 9.81)
+    metrics = {
+        "num_envs": float(base_env.num_envs),
+        "episode_length_s": float(args_cli.episode_length_s),
+        "terrain_size_m": float(args_cli.terrain_size),
+        "success_distance_m": float(args_cli.success_distance),
+        "success_count": float(success_count),
+        "success_rate": float(success_count / success_den),
+        "avg_final_progress_m": float(torch.mean(max_progress).item()),
+        "avg_time_consumption_s": float(avg_time),
+        "time_efficiency": float(avg_time / (args_cli.success_distance / max(abs(args_cli.command_x), 1e-6)))
+        if successful_time.numel() > 0
+        else float("nan"),
+        "mean_lin_vel_error": float(torch.mean(lin_error_sum / max_steps).item()),
+        "mean_yaw_vel_error": float(torch.mean(yaw_error_sum / max_steps).item()),
+        "avg_energy_j": float(torch.mean(energy).item()),
+        "avg_cost_of_transport": float(torch.mean(cot).item()),
+        "unstability_rate": float(torch.mean(instability_count / max_steps).item()),
+        "avg_stability_margin_m": float(torch.mean(margin_sum / max(margin_count, 1)).item()),
+        "avg_max_tilt_rad": float(torch.mean(max_tilt).item()),
+        "avg_stable_time_s": float(torch.mean(stable_time).item()),
+        "avg_support_contact_ratio": float(torch.mean(support_steps / max_steps).item()),
+        "avg_support_events": float(torch.mean(support_events).item()),
+        "avg_max_support_contact_time_s": float(torch.mean(max_support_time).item()),
+        "successful_runs_with_support_ratio": float(
+            torch.mean((support_events[success] > 0.0).float()).item()
+        )
+        if success_count > 0
+        else float("nan"),
+    }
+    if include_per_env:
+        metrics["_per_env"] = {
+            "success": success.detach().cpu().tolist(),
+            "max_progress": max_progress.detach().cpu().tolist(),
+            "max_tilt": max_tilt.detach().cpu().tolist(),
+        }
+    return metrics
+
+
+def make_env_and_policy(env_cfg, agent_cfg, resume_path: str):
+    env = gym.make(args_cli.task, cfg=env_cfg)
+    if isinstance(env.unwrapped, DirectMARLEnv):
+        env = multi_agent_to_single_agent(env)
+    env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
+
+    runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
+    runner.load(resume_path)
+    policy = runner.get_inference_policy(device=env.unwrapped.device)
+    return env, policy
+
+
+def print_upper_limit_matrix(title: str, terrain_types: list[str], matrix: list[list[float | None]]) -> None:
+    """Print an upper-limit success matrix with levels as rows and terrain types as columns."""
+    col_width = 14
+    print(f"\n{title}")
+    print("level".ljust(8) + "".join(terrain_type[: col_width - 1].rjust(col_width) for terrain_type in terrain_types))
+    for level_index, row in enumerate(matrix, start=1):
+        cells = []
+        for value in row:
+            cells.append("   -".rjust(col_width) if value is None else f"{value:.2f}".rjust(col_width))
+        print(f"{level_index:<8}" + "".join(cells))
+
+
+def matrix_to_numpy(matrix: list[list[float | None]]) -> np.ndarray:
+    return np.array([[np.nan if value is None else value for value in row] for row in matrix], dtype=np.float32)
+
+
+def print_numpy_matrix(title: str, matrix: list[list[float | None]]) -> None:
+    print(f"{title}:")
+    print(np.array2string(matrix_to_numpy(matrix).round(2), precision=2, suppress_small=True))
+
+
+def print_upper_limit_summary(result: dict[str, object], terrain_types: list[str]) -> None:
+    matrix = result["_matrix"]
+    print_numpy_matrix("success rate", matrix["success_rate"])
+    print_numpy_matrix("success rate accumulated", matrix["success_rate_accumulated"])
+    score = np.array([result[terrain_type]["score"] for terrain_type in terrain_types], dtype=np.float32)
+    print(f"score: {np.array2string(score.round(2), precision=2, suppress_small=True)} of {len(terrain_types)}")
+
+
+def print_rollout_summary(metrics: dict[str, object]) -> None:
+    success_rate = float(metrics["success_rate"])
+    success_count = int(round(float(metrics["success_count"])))
+    avg_time = float(metrics["avg_time_consumption_s"])
+    avg_time_text = "nan" if np.isnan(avg_time) else f"{avg_time:.2f}"
+    print(f"success rate: {success_rate:.2f}")
+    print(f"success count: {success_count} time consumption: {avg_time_text}")
+    print(f"average power consumption: {float(metrics['avg_energy_j']):.2f}")
+    print(f"average cost of transport: {float(metrics['avg_cost_of_transport']):.2f}")
+    print(f"unstability rate: {float(metrics['unstability_rate']) * 100:.1f}%")
+    print(f"average stability margin: {float(metrics['avg_stability_margin_m']):.2f}")
+    print(
+        f"average max tilt: {float(metrics['avg_max_tilt_rad']):.2f} "
+        f"average max time: {float(metrics['avg_stable_time_s']):.2f}"
+    )
+    support_success_ratio = float(metrics["successful_runs_with_support_ratio"])
+    support_success_text = "nan" if np.isnan(support_success_ratio) else f"{support_success_ratio * 100:.1f}%"
+    print(
+        f"support contact ratio: {float(metrics['avg_support_contact_ratio']) * 100:.1f}% "
+        f"events/episode: {float(metrics['avg_support_events']):.2f} "
+        f"max duration: {float(metrics['avg_max_support_contact_time_s']):.2f}s "
+        f"successful runs using support: {support_success_text}"
+    )
+
+
+def print_metric_result(eval_type: str, metrics: dict[str, object]) -> None:
+    if eval_type == "velocity_error":
+        print(f"mean lin vel error: {float(metrics['mean_lin_vel_error']):.2f}")
+        print(f"mean yaw vel error: {float(metrics['mean_yaw_vel_error']):.2f}")
+    elif eval_type == "access_rate":
+        print(f"success rate: {float(metrics['success_rate']):.2f}")
+    elif eval_type == "time_consumption":
+        success_count = int(round(float(metrics["success_count"])))
+        avg_time = float(metrics["avg_time_consumption_s"])
+        avg_time_text = "nan" if np.isnan(avg_time) else f"{avg_time:.2f}"
+        print(f"success count: {success_count} time consumption: {avg_time_text}")
+    elif eval_type == "energy_consumption":
+        print(f"average power consumption: {float(metrics['avg_energy_j']):.2f}")
+        print(f"average cost of transport: {float(metrics['avg_cost_of_transport']):.2f}")
+    elif eval_type == "motion_stability":
+        print(f"unstability rate: {float(metrics['unstability_rate']) * 100:.1f}%")
+    elif eval_type == "stability_margin":
+        print(f"average stability margin: {float(metrics['avg_stability_margin_m']):.2f}")
+    elif eval_type == "robustness":
+        print(
+            f"average max tilt: {float(metrics['avg_max_tilt_rad']):.2f} "
+            f"average max time: {float(metrics['avg_stable_time_s']):.2f}"
+        )
+
+
+def run_upper_limits(env_cfg, agent_cfg, resume_path: str) -> dict[str, object]:
+    terrain_types = list(TERRAIN_LEVELS) if args_cli.terrain_type == "all" else [args_cli.terrain_type]
+    eval_cfg = copy.deepcopy(env_cfg)
+    configure_upper_limit_env(eval_cfg, terrain_types)
+    env, policy = make_env_and_policy(eval_cfg, agent_cfg, resume_path)
+    level_ids, type_ids = assign_upper_limit_origins(env, terrain_types)
+    metrics = run_rollout(env, policy, include_per_env=True)
+    env.close()
+
+    per_env = metrics.pop("_per_env")
+    success = torch.tensor(per_env["success"], dtype=torch.float32)
+    max_progress = torch.tensor(per_env["max_progress"], dtype=torch.float32)
+    max_tilt = torch.tensor(per_env["max_tilt"], dtype=torch.float32)
+    level_ids_cpu = level_ids.cpu()
+    type_ids_cpu = type_ids.cpu()
+
+    result: dict[str, object] = {}
+    for terrain_type_id, terrain_type in enumerate(terrain_types):
+        level_values = TERRAIN_LEVELS[terrain_type][: args_cli.num_levels]
+        rates = []
+        level_metrics = []
+        for level_index, level_value in enumerate(level_values):
+            mask = (type_ids_cpu == terrain_type_id) & (level_ids_cpu == level_index)
+            success_count = float(torch.sum(success[mask]).item())
+            total_count = int(torch.sum(mask).item())
+            success_rate = success_count / max(total_count, 1)
+            avg_progress = float(torch.mean(max_progress[mask]).item()) if total_count > 0 else float("nan")
+            avg_tilt = float(torch.mean(max_tilt[mask]).item()) if total_count > 0 else float("nan")
+            rates.append(success_rate)
+            level_metrics.append(
+                {
+                    "level": level_index + 1,
+                    "difficulty": level_value,
+                    "success_rate": success_rate,
+                    "success_count": success_count,
+                    "num_envs": total_count,
+                    "avg_progress_m": avg_progress,
+                    "avg_max_tilt_rad": avg_tilt,
+                }
+            )
+            if args_cli.verbose:
+                print(
+                    f"[EVAL] {terrain_type} level={level_index + 1} difficulty={level_value:.3f} "
+                    f"success_rate={success_rate:.3f}"
+                )
+        accumulated = []
+        running = 1.0
+        for rate in rates:
+            running *= rate
+            accumulated.append(running)
+        score = 0.0
+        for idx, acc_rate in enumerate(accumulated):
+            next_fail = 1.0 - rates[idx + 1] if idx + 1 < len(rates) else 1.0
+            score += acc_rate * (idx + 1) * next_fail
+        result[terrain_type] = {
+            "levels": level_metrics,
+            "success_rate": rates,
+            "success_rate_accumulated": accumulated,
+            "score": score,
+            "max_level": len(level_values),
+            "benchmark": "fixed_rough_terrain_v1",
+            "terrain_size_m": args_cli.terrain_size,
+            "success_distance_m": args_cli.success_distance,
+            "episode_length_s": args_cli.episode_length_s,
+        }
+    max_levels = max((len(result[terrain_type]["success_rate"]) for terrain_type in terrain_types), default=0)
+    success_matrix: list[list[float | None]] = []
+    accumulated_matrix: list[list[float | None]] = []
+    difficulty_matrix: list[list[float | None]] = []
+    for level_index in range(max_levels):
+        success_row = []
+        accumulated_row = []
+        difficulty_row = []
+        for terrain_type in terrain_types:
+            rates = result[terrain_type]["success_rate"]
+            accumulated = result[terrain_type]["success_rate_accumulated"]
+            levels = result[terrain_type]["levels"]
+            success_row.append(rates[level_index] if level_index < len(rates) else None)
+            accumulated_row.append(accumulated[level_index] if level_index < len(accumulated) else None)
+            difficulty_row.append(levels[level_index]["difficulty"] if level_index < len(levels) else None)
+        success_matrix.append(success_row)
+        accumulated_matrix.append(accumulated_row)
+        difficulty_matrix.append(difficulty_row)
+
+    result["_matrix"] = {
+        "terrain_types": terrain_types,
+        "success_rate": success_matrix,
+        "success_rate_accumulated": accumulated_matrix,
+        "difficulty": difficulty_matrix,
+        "terrain_size_m": args_cli.terrain_size,
+        "success_distance_m": args_cli.success_distance,
+        "episode_length_s": args_cli.episode_length_s,
+    }
+    result["_summary"] = metrics
+    print_upper_limit_summary(result, terrain_types)
+    return result
+
+
+def filter_metrics(metrics: dict[str, float], eval_type: str) -> dict[str, float]:
+    if eval_type == "all":
+        return metrics
+    keys = {
+        "velocity_error": ("mean_lin_vel_error", "mean_yaw_vel_error"),
+        "access_rate": ("success_count", "success_rate", "avg_final_progress_m"),
+        "time_consumption": ("success_count", "avg_time_consumption_s", "time_efficiency"),
+        "energy_consumption": ("avg_energy_j", "avg_cost_of_transport"),
+        "motion_stability": ("unstability_rate",),
+        "stability_margin": ("avg_stability_margin_m",),
+        "robustness": ("success_rate", "avg_max_tilt_rad", "avg_stable_time_s"),
+    }[eval_type]
+    return {key: metrics[key] for key in keys}
+
+
+@hydra_task_config(args_cli.task, args_cli.agent)
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlOnPolicyRunnerCfg):
+    agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
+
+    log_root_path = os.path.abspath(os.path.join("logs", "rsl_rl", agent_cfg.experiment_name))
+    resume_path = resolve_checkpoint(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
+    print(f"[INFO] Evaluating checkpoint: {resume_path}")
+
+    if args_cli.eval_type == "upper_limits":
+        selected = run_upper_limits(env_cfg, agent_cfg, resume_path)
+    elif args_cli.eval_type == "all":
+        upper_limits = run_upper_limits(env_cfg, agent_cfg, resume_path)
+        selected = {"upper_limits": upper_limits}
+        print_rollout_summary(upper_limits["_summary"])
+        if args_cli.include_summary:
+            summary_cfg = copy.deepcopy(env_cfg)
+            configure_eval_env(summary_cfg, "all")
+            env, policy = make_env_and_policy(summary_cfg, agent_cfg, resume_path)
+            summary = run_rollout(env, policy)
+            env.close()
+            selected["summary"] = summary
+    else:
+        eval_type_for_env = "robustness" if args_cli.eval_type == "robustness" else "all"
+        configure_eval_env(env_cfg, eval_type_for_env)
+        env, policy = make_env_and_policy(env_cfg, agent_cfg, resume_path)
+        metrics = run_rollout(env, policy)
+        selected = filter_metrics(metrics, args_cli.eval_type)
+        env.close()
+        print_metric_result(args_cli.eval_type, selected)
+    if args_cli.print_json:
+        print(json.dumps(selected, indent=2, sort_keys=True))
+
+    output = args_cli.output
+    if output is None:
+        eval_dir = os.path.join(os.path.dirname(resume_path), "eval")
+        os.makedirs(eval_dir, exist_ok=True)
+        suffix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        output = os.path.join(eval_dir, f"{args_cli.eval_type}_{suffix}.json")
+    else:
+        output = os.path.abspath(os.path.expanduser(output))
+        output_dir = os.path.dirname(output)
+        if output_dir:
+            os.makedirs(output_dir, exist_ok=True)
+    with open(output, "w", encoding="utf-8") as f:
+        json.dump({"eval_type": args_cli.eval_type, "checkpoint": resume_path, "metrics": selected}, f, indent=2)
+    print(f"[INFO] Saved evaluation result to: {output}")
+
+
+if __name__ == "__main__":
+    main()
+    simulation_app.close()
diff --git a/scripts/reinforcement_learning/rsl_rl/eval.sh b/scripts/reinforcement_learning/rsl_rl/eval.sh
new file mode 100755
index 0000000..cf9730b
--- /dev/null
+++ b/scripts/reinforcement_learning/rsl_rl/eval.sh
@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+TASK=${TASK:-Rough-Deeprobotics-M20-v0}
+CHECKPOINT=${1:-${CHECKPOINT:-}}
+NUM_ENVS=${NUM_ENVS:-250}
+DEVICE=${DEVICE:-cuda:0}
+EVAL_TYPE=${EVAL_TYPE:-all}
+
+if [[ -z "${CHECKPOINT}" ]]; then
+  echo "Usage: $0 <checkpoint.pt>"
+  echo "Or set CHECKPOINT=/path/to/model.pt"
+  exit 1
+fi
+
+echo "Running eval_type=${EVAL_TYPE}"
+python scripts/reinforcement_learning/rsl_rl/eval.py \
+  --task="${TASK}" \
+  --checkpoint="${CHECKPOINT}" \
+  --num_envs="${NUM_ENVS}" \
+  --device="${DEVICE}" \
+  --eval_type="${EVAL_TYPE}" \
+  --headless
diff --git a/scripts/reinforcement_learning/rsl_rl/eval_suite.py b/scripts/reinforcement_learning/rsl_rl/eval_suite.py
new file mode 100644
index 0000000..1a97dd4
--- /dev/null
+++ b/scripts/reinforcement_learning/rsl_rl/eval_suite.py
@@ -0,0 +1,893 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""评价套件的命令行入口。
+
+与 ``eval.py`` 并存、互不影响：本脚本不导入也不修改 ``eval.py``，两者可以并行使用，
+输出 JSON 结构相似（指标名完全一致），便于互相对照。
+
+用法示例::
+
+    # 标准评估（默认协议与火蚁描述）
+    python scripts/reinforcement_learning/rsl_rl/eval_suite.py \\
+        --task Rough-Deeprobotics-M20-v0 \\
+        --checkpoint /绝对路径/model_19999.pt \\
+        --headless
+
+    # 上限评估，5 个随机种子，顺带把轨迹存下来
+    python scripts/reinforcement_learning/rsl_rl/eval_suite.py \\
+        --task Rough-Deeprobotics-M20-v0 \\
+        --checkpoint /绝对路径/model_19999.pt \\
+        --protocol standard_v1_multiseed --save-trace /tmp/traces --headless
+
+    # 抗扰评估
+    python scripts/reinforcement_learning/rsl_rl/eval_suite.py \\
+        --task Rough-Deeprobotics-M20-v0 \\
+        --checkpoint /绝对路径/model_19999.pt \\
+        --eval-type robustness --headless
+
+    # 用 WebRTC 看画面（不需要显示器；局域网内用 Isaac Sim WebRTC Streaming Client 连）
+    python scripts/reinforcement_learning/rsl_rl/eval_suite.py \\
+        --task Rough-Deeprobotics-M20-v0 \\
+        --checkpoint /绝对路径/model_19999.pt \\
+        --eval-type upper_limits --terrain-type stairs_up \\
+        --livestream 2
+
+    ``--livestream 1`` 走公网（NVIDIA NVCF 信令，需要 ``PUBLIC_IP``），
+    ``--livestream 2`` 走局域网直连；两者都由 ``AppLauncher`` 提供，详见
+    ``isaaclab.app.AppLauncher`` 的文档。本脚本只额外保证：视口相机跟着被评估的
+    机器人（``--view-env``）、开始 rollout 前留出连接时间（``--pause-s``）、
+    以及把「客户端该连哪里」打印出来。
+
+    协议预设与机器人描述都在 ``eval_kit/`` 里：改口径改 ``protocols.py``，
+    加机器人改 ``profiles.py``。
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import json
+import os
+import subprocess
+import sys
+import tempfile
+from dataclasses import replace
+from typing import Any
+
+#: 原始命令行，用于在需要多个环境时 fork 子进程（见文件末尾的说明）。
+_ORIGINAL_ARGV = list(sys.argv[1:])
+
+# --- 路径引导：必须在导入 eval_kit / rl_training 之前完成 --------------------------
+_HERE = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.abspath(os.path.join(_HERE, "..", "..", ".."))
+PROJECT_SOURCE_PATH = os.path.join(PROJECT_ROOT, "source", "rl_training")
+for path in (PROJECT_SOURCE_PATH, PROJECT_ROOT):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+from isaaclab.app import AppLauncher  # noqa: E402
+
+from eval_kit.core.report import (  # noqa: E402
+    EVAL_TYPES,
+    filter_metrics,
+    format_aggregate_summary,
+    format_metric_result,
+    format_rollout_summary,
+    format_upper_limit_summary,
+)
+from eval_kit.core.schema import EvalProtocol  # noqa: E402
+from eval_kit.core.terrain_spec import get_terrain_spec  # noqa: E402
+from eval_kit.profiles import PROFILES, get_profile  # noqa: E402
+from eval_kit.protocols import PROTOCOLS, get_protocol  # noqa: E402
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="基于 eval_kit 的机器人性能评估（与 eval.py 并存，指标口径一致）。",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--task", type=str, default="Rough-Deeprobotics-M20-v0", help="任务名。")
+    parser.add_argument(
+        "--agent", type=str, default="rsl_rl_cfg_entry_point", help="agent 配置入口名（配合 Hydra）。"
+    )
+    parser.add_argument(
+        "--profile", type=str, default="solenopsis", choices=sorted(PROFILES), help="机器人形态描述。"
+    )
+    parser.add_argument(
+        "--protocol", type=str, default="standard_v1", choices=sorted(PROTOCOLS), help="评估协议预设。"
+    )
+    parser.add_argument(
+        "--eval-type", dest="eval_type", type=str, default="all", choices=list(EVAL_TYPES), help="评估类型。"
+    )
+
+    checkpoint = parser.add_argument_group("checkpoint")
+    checkpoint.add_argument("--checkpoint", type=str, default=None, help="checkpoint 路径（支持绝对路径）。")
+    checkpoint.add_argument("--load-run", dest="load_run", type=str, default=None, help="运行目录或目录名。")
+    checkpoint.add_argument(
+        "--experiment-name", dest="experiment_name", type=str, default=None,
+        help="实验名，用于在 logs/rsl_rl/<实验名> 下查找；不传则用 agent 配置里的。",
+    )
+
+    run = parser.add_argument_group("运行规模（不传则取协议预设值）")
+    run.add_argument(
+        "--num-envs", dest="num_envs", type=int, default=250,
+        help="并行环境数（上限评估时会按网格规模自动覆盖）。",
+    )
+    # 注意：--device 由 AppLauncher.add_app_launcher_args() 提供，这里不能再加。
+    # 它保留了 device / headless / enable_cameras / experience / livestream /
+    # rendering_mode / xr 这几个名字，重复定义会直接抛 ValueError。
+    run.add_argument(
+        "--seeds", type=int, nargs="+", default=None,
+        help="重复试验的随机种子，覆盖协议预设；给多个种子时会输出 mean±std。",
+    )
+    run.add_argument("--save-trace", dest="save_trace", type=str, default=None, help="把轨迹存到该目录。")
+    run.add_argument(
+        "--view-env", dest="view_env", type=int, default=None,
+        help="让视口相机跟随第 N 个环境的机器人（非 headless 或开启推流时有效）。"
+             "不传时默认跟随 env 0。环境编号 0 起，"
+             "编号与（地形, 难度）的对应关系会在启动时打印。",
+    )
+    run.add_argument(
+        "--free-camera", dest="free_camera", action="store_true", default=False,
+        help="不要让视口相机跟随任何机器人：起始摆在俯视全场的位置，之后在 WebRTC 客户端里"
+             "自己移动（脚本与 IsaacLab 都不会再改写相机）。同时给了 --view-env 时以本参数为准。",
+    )
+    run.add_argument(
+        "--pause-s", dest="pause_s", type=float, default=None,
+        help="开始 rollout 之前停顿的秒数：期间画面照常推流、物理冻结，按回车可提前开始。"
+             "用来给 WebRTC 客户端留连接时间。开启推流时默认 30 s，其余情况默认 0（不停顿）。",
+    )
+    run.add_argument(
+        "--record-frames", dest="record_frames", type=str, default=None,
+        help="把 rollout 期间的视口画面按帧存成 PNG 到该目录（例如 frame_000150.png）。"
+             "推流传输不可用时用它离线看评估过程；抓帧间隔见 --record-every。",
+    )
+    run.add_argument(
+        "--record-every", dest="record_every", type=int, default=5,
+        help="--record-frames 每多少步抓一帧。",
+    )
+
+    overrides = parser.add_argument_group("口径覆盖（不传则取协议预设值，改动会反映到 fingerprint）")
+    overrides.add_argument("--episode-length-s", dest="episode_length_s", type=float, default=None)
+    overrides.add_argument("--command-x", dest="command_x", type=float, default=None)
+    overrides.add_argument("--command-y", dest="command_y", type=float, default=None)
+    overrides.add_argument("--command-yaw", dest="command_yaw", type=float, default=None)
+    overrides.add_argument("--start-x", dest="start_x", type=float, default=None)
+    overrides.add_argument("--start-y", dest="start_y", type=float, default=None)
+    overrides.add_argument(
+        "--start-z-offset", dest="start_z_offset", type=float, default=None,
+        help="起始高度相对地形瓦片中心的抬升 [m]。倒金字塔台阶这类「中心最低」的地形需要正值，"
+             "否则机器人会被生成在地形内部（默认 0.0，与旧 eval.py 一致）。",
+    )
+    overrides.add_argument("--terrain-size", dest="terrain_size", type=float, default=None, help="地形瓦片边长 [m]。")
+    overrides.add_argument("--success-distance", dest="success_distance", type=float, default=None)
+    overrides.add_argument("--tilt-threshold", dest="tilt_threshold", type=float, default=None, help="摔倒倾角 [rad]。")
+    overrides.add_argument(
+        "--acc-threshold-g", dest="acc_threshold_g", type=float, default=None,
+        help="不稳定判定的加速度阈值，单位 g。",
+    )
+    overrides.add_argument(
+        "--energy-convention", dest="energy_convention", type=str, default=None, choices=["absolute", "signed"]
+    )
+    overrides.add_argument(
+        "--robot-mass", dest="robot_mass", type=float, default=None,
+        help="覆盖 COT 用质量；默认从资产自动读取。",
+    )
+    overrides.add_argument(
+        "--terrain-type", dest="terrain_type", type=str, default="all",
+        help="上限评估只用某一种地形：all 或地形规格里的类型名。",
+    )
+    overrides.add_argument("--num-levels", dest="num_levels", type=int, default=None, help="上限评估的难度等级数。")
+    overrides.add_argument("--envs-per-cell", dest="envs_per_cell", type=int, default=None, help="每格机器人数。")
+    overrides.add_argument(
+        "--no-terrain-bounds", dest="enforce_terrain_bounds", action="store_false", default=None,
+        help="去掉越界终止项。地砖小于 3.8 m 时它会误判最外侧格子（详见 --help 里 protocol 的说明）。",
+    )
+
+    output = parser.add_argument_group("输出")
+    output.add_argument("--output", type=str, default=None, help="输出 JSON 路径；默认写在 checkpoint 同目录的 eval_kit/ 下。")
+    output.add_argument("--print-json", dest="print_json", action="store_true", default=False, help="把 JSON 打到 stdout。")
+    # 不能叫 --verbose：AppLauncher.add_app_launcher_args() 已经占用了这个名字，
+    # 重复定义会让 argparse 直接抛 ArgumentError（旧 eval.py 就卡在这里）。
+    output.add_argument(
+        "--verbose-terrain", dest="verbose_terrain", action="store_true", default=False,
+        help="打印逐格地形/难度调试信息。（注意 --verbose 属于 AppLauncher，行为不同）",
+    )
+    output.add_argument(
+        "--include-summary", dest="include_summary", action="store_true", default=False,
+        help="eval-type=all 时额外跑一次混合地形汇总。",
+    )
+    # 内部参数：一个进程只能建一个环境，需要多个环境时由父进程 fork 自己并传这些参数。
+    # 不建议手工使用。
+    internal = parser.add_argument_group("内部（子进程用，勿手工指定）")
+    internal.add_argument("--stage", type=str, default="", choices=["", "upper", "plain"])
+    internal.add_argument("--stage-seed", dest="stage_seed", type=int, default=0)
+    internal.add_argument("--stage-out", dest="stage_out", type=str, default=None)
+    return parser
+
+
+def _apply_overrides(protocol: EvalProtocol, args: argparse.Namespace) -> EvalProtocol:
+    """把命令行显式给出的口径覆盖到协议预设上。没给的不动，保持预设原值。"""
+    updates: dict[str, object] = {}
+    if args.episode_length_s is not None:
+        updates["steps"] = int(round(args.episode_length_s / protocol.dt))
+    if args.command_x is not None:
+        updates["command"] = (args.command_x, protocol.command[1], protocol.command[2])
+    if args.command_y is not None:
+        current = updates.get("command", protocol.command)
+        updates["command"] = (current[0], args.command_y, current[2])
+    if args.command_yaw is not None:
+        current = updates.get("command", protocol.command)
+        updates["command"] = (current[0], current[1], args.command_yaw)
+    if args.start_x is not None:
+        updates["start_pose"] = (args.start_x, protocol.start_pose[1], protocol.start_pose[2])
+    if args.start_y is not None:
+        current = updates.get("start_pose", protocol.start_pose)
+        updates["start_pose"] = (current[0], args.start_y, current[2])
+    if args.start_z_offset is not None:
+        updates["start_z_offset_m"] = args.start_z_offset
+    if args.terrain_size is not None:
+        updates["terrain_size_m"] = args.terrain_size
+    if args.success_distance is not None:
+        updates["success_distance_m"] = args.success_distance
+    if args.tilt_threshold is not None:
+        updates["tilt_threshold_rad"] = args.tilt_threshold
+    if args.acc_threshold_g is not None:
+        updates["acc_threshold_g"] = args.acc_threshold_g
+    if args.energy_convention is not None:
+        updates["energy_convention"] = args.energy_convention
+    if args.num_levels is not None:
+        updates["num_levels"] = args.num_levels
+    if args.envs_per_cell is not None:
+        updates["envs_per_cell"] = args.envs_per_cell
+    if args.seeds is not None:
+        updates["seeds"] = tuple(args.seeds)
+    if args.enforce_terrain_bounds is not None:
+        updates["enforce_terrain_bounds"] = args.enforce_terrain_bounds
+    return replace(protocol, **updates)  # type: ignore[arg-type]
+
+
+def _terrain_types_for(args: argparse.Namespace, spec) -> list[str]:
+    if args.terrain_type == "all":
+        return list(spec.types)
+    if args.terrain_type not in spec.types:
+        raise SystemExit(
+            f"地形类型 {args.terrain_type!r} 不在规格 {spec.name!r} 里；可选：{list(spec.types)}"
+        )
+    return [args.terrain_type]
+
+
+def _resolve_output_path(args: argparse.Namespace, resume_path: str, eval_type: str) -> str:
+    if args.output is not None:
+        path = os.path.abspath(os.path.expanduser(args.output))
+    else:
+        from datetime import datetime
+
+        directory = os.path.join(os.path.dirname(resume_path), "eval_kit")
+        suffix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        path = os.path.join(directory, f"{eval_type}_{suffix}.json")
+    parent = os.path.dirname(path)
+    if parent:
+        os.makedirs(parent, exist_ok=True)
+    return path
+
+
+def _livestream_value(args_cli: argparse.Namespace) -> int:
+    """解析实际生效的推流模式：0 关、1 公网 WebRTC、2 局域网 WebRTC。
+
+    ``--livestream`` 的默认值是 -1，含义是「听 ``LIVESTREAM`` 环境变量的」，
+    所以 -1 时要回退去读环境变量，而不是当成 0。
+    """
+    try:
+        value = int(getattr(args_cli, "livestream", -1))
+    except (TypeError, ValueError):
+        value = -1
+    if value < 0:
+        try:
+            value = int(os.environ.get("LIVESTREAM", 0) or 0)
+        except ValueError:
+            value = 0
+    return value
+
+
+def _display_enabled(args_cli: argparse.Namespace) -> bool:
+    """这次运行会不会给用户看画面（本地 GUI 或推流）。"""
+    return not getattr(args_cli, "headless", True) or _livestream_value(args_cli) > 0
+
+
+def _viewer_env_index(args_cli: argparse.Namespace) -> int | None:
+    """视口相机跟随哪个环境；不跟随时（自由视角、或完全不需要画面）返回 None。
+
+    必须在构造 ``AppLauncher`` **之前**调用：``AppLauncher`` 会把 ``livestream``
+    从 namespace 里 pop 掉，还会在开启推流时把 ``headless`` 改写成 True（推流在
+    服务端本来就按 headless 启动），等它跑完再读这两个值就分不清「用户要不要看画面」了。
+
+    推流时 ``SimulationContext`` 的 ``has_gui()``（本地 GUI 或推流 GUI）仍为真、
+    ``env_cfg.viewer`` 也仍然生效，所以推流和本地 GUI 两种情况都要设视口相机，
+    否则推流画面停在默认相机上、看不到机器人。
+    """
+    if not _display_enabled(args_cli) or getattr(args_cli, "free_camera", False):
+        return None
+    return 0 if args_cli.view_env is None else int(args_cli.view_env)
+
+
+def _set_free_camera_overview(env: Any) -> None:
+    """自由视角模式：把相机摆到能看见整片地形的位置。
+
+    ``env_cfg.viewer.origin_type`` 是 ``world`` 时，``ViewportCameraController``
+    只在初始化时设一次相机、之后不再改写，所以这里设的视角会一直保留，
+    客户端里可以随便移动鼠标/键盘来观察。
+    """
+    import numpy as np
+
+    origins = env.unwrapped.scene.env_origins
+    center = origins.mean(dim=0)
+    radius = float((origins - center).norm(dim=1).max().item())
+    center_np = center.detach().cpu().numpy()
+    eye = center_np + np.array([0.0, -1.6 * radius, 1.25 * radius], dtype=center_np.dtype)
+    env.unwrapped.sim.set_camera_view(eye=eye, target=center_np)
+    print(
+        f"[eval_kit] 自由视角：相机不跟随机器人，起始俯视全场"
+        f"（地形中心 {np.round(center_np, 1).tolist()}，半径 {radius:.1f} m），"
+        f"之后在客户端里自己移动",
+        flush=True,
+    )
+
+
+def _allow_client_resize(args_cli: argparse.Namespace) -> None:
+    """让 WebRTC 客户端可以改画面尺寸（默认关掉会冻住画面）。
+
+    客户端一旦改窗口大小/全屏，就会发 ``onCustomResizeMessage``；服务端默认拒绝
+    （``... must both be positive and /app/livestream/allowDynamicResize enabled``），
+    同时推流用的 ``shared swapchain buffer`` 进入坏状态 —— 之后画面不再更新，而仿真
+    照常在跑（终端里的 rollout 进度还在涨）。这两个设置通过 ``--kit_args`` 传给 Kit。
+
+    用户自己显式给了同名设置就不覆盖（想关掉就传 ``--kit_args=--/app/livestream/allowDynamicResize=false``）。
+    """
+    wanted = ("--/app/livestream/allowResize=true", "--/app/livestream/allowDynamicResize=true")
+    existing = args_cli.kit_args or ""
+    extra = [flag for flag in wanted if flag.split("=")[0] not in existing]
+    if not extra:
+        return
+    args_cli.kit_args = " ".join(filter(None, [existing, *extra]))
+    print(
+        f"[eval_kit] 推流追加 kit 设置：{' '.join(extra)}（允许客户端调整窗口大小/全屏）",
+        flush=True,
+    )
+
+
+def _grab_viewport_frame(state: dict, directory: str, every: int) -> None:
+    """每 ``every`` 步向视口要一张 PNG。
+
+    抓帧是异步的（等下一次渲染产出），所以这里不阻塞 rollout：上一张还没写完就跳过
+    这一帧。写完的文件名形如 ``frame_000150.png``，可用 ffmpeg 合成视频。
+    """
+    if every <= 0 or state["step"] % every:
+        return
+    pending = state["pending"]
+    if pending is not None and not os.path.exists(pending):
+        return
+    from omni.kit.viewport.utility import capture_viewport_to_file, get_active_viewport
+
+    path = os.path.join(directory, f"frame_{state['step']:06d}.png")
+    state["pending"] = path
+    capture_viewport_to_file(get_active_viewport(), path)
+
+
+def _with_frame_capture(policy: Any, directory: str, every: int) -> Any:
+    """把策略包一层，顺带按间隔抓视口帧（策略每被调用一次就是 rollout 推进一步）。"""
+    os.makedirs(directory, exist_ok=True)
+    state: dict = {"step": 0, "pending": None}
+    print(f"[eval_kit] rollout 期间抓帧到 {directory}（每 {every} 步一张 PNG）", flush=True)
+
+    def wrapped(obs: Any) -> Any:
+        state["step"] += 1
+        _grab_viewport_frame(state, directory, every)
+        return policy(obs)
+
+    return wrapped
+
+
+def _report_recorded_frames(directory: str, every: int, dt: float) -> None:
+    """抓帧结束后提示怎么合成视频（帧是按仿真时间等间隔抓的，fps = 1/(every*dt)）。"""
+    frames = sorted(name for name in os.listdir(directory) if name.endswith(".png"))
+    if not frames:
+        print(f"[eval_kit] 抓帧目录 {directory} 里没有帧（视口没渲染？）", flush=True)
+        return
+    fps = 1.0 / (every * dt) if every > 0 and dt > 0 else 10.0
+    print(f"[eval_kit] 抓到 {len(frames)} 帧：{directory}", flush=True)
+    print(
+        f"[eval_kit]   合成视频（按仿真速度播放）：cd {directory} && "
+        f"ffmpeg -y -framerate {fps:.2f} -pattern_type glob -i '*.png' "
+        f"-pix_fmt yuv420p -c:v libx264 -vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' /tmp/eval.mp4",
+        flush=True,
+    )
+
+
+def _local_ipv4_addresses() -> list[str]:
+    """本机的非回环 IPv4 地址，用来告诉用户 WebRTC 客户端该连哪里。"""
+    import socket
+
+    addresses: list[str] = []
+    try:
+        for info in socket.getaddrinfo(socket.gethostname(), None, socket.AF_INET):
+            address = info[4][0]
+            if address not in addresses:
+                addresses.append(address)
+    except OSError:
+        pass
+    try:
+        # 不真的发包：只是让内核按路由表挑一个默认出口地址
+        with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as probe:
+            probe.connect(("8.8.8.8", 80))
+            address = probe.getsockname()[0]
+            if address not in addresses:
+                addresses.append(address)
+    except OSError:
+        pass
+    return [address for address in addresses if not address.startswith("127.")]
+
+
+def _print_livestream_info(livestream: int) -> None:
+    """打印客户端该连到哪里。要在 Kit 起来之后调用，端口从 carb 设置里读实际值。"""
+    import carb.settings
+
+    port = carb.settings.get_settings().get("/app/livestream/port")
+    addresses = _local_ipv4_addresses()
+    if livestream == 1:
+        public_ip = os.environ.get("PUBLIC_IP") or (addresses[0] if addresses else "127.0.0.1")
+        print(f"[eval_kit] WebRTC 推流已启用（--livestream 1，公网，PUBLIC_IP={public_ip}）", flush=True)
+    else:
+        print("[eval_kit] WebRTC 推流已启用（--livestream 2，局域网直连）", flush=True)
+    if addresses:
+        targets = "  ".join(f"{address}:{port}" for address in addresses)
+        print(f"[eval_kit]   客户端连接地址：{targets}", flush=True)
+    else:
+        print(f"[eval_kit]   客户端连接地址：<本机 IP>:{port}", flush=True)
+    print(
+        "[eval_kit]   本子进程结束后推流会断开（每个阶段/种子都是一个新进程），"
+        "客户端重连即可",
+        flush=True,
+    )
+
+
+def _hold_before_rollout(sim: Any, seconds: float, *, start_height_note: str) -> None:
+    """停顿 ``seconds`` 秒再开始 rollout：画面继续推流、物理冻结。
+
+    用 ``sim.render()`` 而不是 ``time.sleep``：独立进程模式下 Kit 的更新循环由我们
+    调用 ``app.update()`` 驱动，光 sleep 推流画面会冻住。``render()`` 内部会先把
+    ``/app/player/playSimulations`` 关掉再 update，因此画面在刷新、时间不前进。
+    另外把 timeline 暂停，避免物理在等待期间自己走掉（那会让评估起点不是刚重置的状态）。
+    """
+    import select
+    import time as _time
+
+    sim.pause()
+    started = _time.monotonic()
+    deadline = started + seconds
+    # 明确说明「悬空不动」是正常的：这一窗物理是冻结的，机器人正停在法规的起始位姿上
+    print(
+        f"[eval_kit] 画面已就绪，先停 {seconds:.0f} s 再开始 rollout —— 期间物理是冻结的，"
+        f"机器人在起始位姿上不动{start_height_note}，看起来「悬空不动」是正常的。",
+        flush=True,
+    )
+    if sys.stdin is not None and sys.stdin.isatty():
+        print("[eval_kit]   在终端按回车可立即开始 rollout", flush=True)
+    next_report = started + 5.0
+    while _time.monotonic() < deadline:
+        sim.render()
+        if sys.stdin is not None and sys.stdin.isatty() and select.select([sys.stdin], [], [], 0)[0]:
+            sys.stdin.readline()
+            break
+        if _time.monotonic() >= next_report:
+            print(f"[eval_kit]   剩余 {max(0.0, deadline - _time.monotonic()):.0f} s（物理仍冻结）", flush=True)
+            next_report += 5.0
+    sim.play()
+    print(f"[eval_kit] 停顿 {_time.monotonic() - started:.1f} s 结束，开始 rollout（画面从这里才动）", flush=True)
+
+
+def _format_early_reset_warning(early: dict, protocol: EvalProtocol, min_terrain_size: float) -> str:
+    """把早退统计变成可执行的建议。"""
+    import json as _json
+
+    lines = [
+        "[警告] 有环境在 episode 中途被重置，路程/进度类指标可能被污染："
+        f"{_json.dumps(early, ensure_ascii=False)}"
+    ]
+    if protocol.enforce_terrain_bounds:
+        lines.append("  最常见的原因是 IsaacLab 的 terrain_out_of_bounds 终止项：它比较机器人的")
+        lines.append("  世界坐标与整张地图的范围，而不是机器人自己那块地砖，于是最外侧的格子")
+        lines.append(f"  从第 0 步起就被判越界。当前地砖 {protocol.terrain_size_m} m，")
+        lines.append(f"  要保留该终止项需要 >= {min_terrain_size:.1f} m。三种处置：")
+        lines.append(f"    1) --terrain-size {max(min_terrain_size, protocol.terrain_size_m):.1f}  保留终止项，消除误判")
+        lines.append("    2) --no-terrain-bounds         去掉该终止项")
+        lines.append("    3) --protocol standard_v1_clean  等价于方案 1")
+    return "\n".join(lines)
+
+
+def _stage_argv(stage: str, seed: int, out_path: str) -> list[str]:
+    """构造子进程命令行：在原始参数上追加阶段参数。"""
+    return [
+        sys.executable,
+        os.path.abspath(__file__),
+        *_ORIGINAL_ARGV,
+        "--stage", stage,
+        "--stage-seed", str(seed),
+        "--stage-out", out_path,
+    ]
+
+
+def _spawn_stage(stage: str, seed: int) -> dict[str, object]:
+    """在子进程里跑一个「一个环境」的阶段，读回它写的 JSON。
+
+    为什么必须开子进程：IsaacLab 在本配置下一个进程只能创建一个环境。第一个环境
+    建好之后，``ROUGH_TERRAINS_CFG`` 这类模块级单例会被运行时长出自引用，之后
+    任何 cfg 构造（新实例 ``env_cfg_cls()``、开场存的 pristine 深拷贝、重新深拷贝）
+    都会在 ``cfg.validate()`` 里 RecursionError。三种补救方式都实测失败，所以只能
+    一个环境一个进程。
+    """
+    handle = tempfile.NamedTemporaryFile(prefix=f"eval_kit_{stage}_", suffix=".json", delete=False)
+    handle.close()
+    out_path = handle.name
+    argv = _stage_argv(stage, seed, out_path)
+    print(f"[eval_kit] 子进程 stage={stage} seed={seed}", flush=True)
+    completed = subprocess.run(argv)
+    if completed.returncode != 0:
+        os.unlink(out_path)
+        raise SystemExit(f"子进程 stage={stage} seed={seed} 失败（退出码 {completed.returncode}）")
+    try:
+        with open(out_path, encoding="utf-8") as file_handle:
+            return json.load(file_handle)
+    finally:
+        os.unlink(out_path)
+
+
+def _recorder_report(recorder) -> dict[str, object]:
+    return {
+        "feet": recorder.feet_names if recorder else [],
+        "support": recorder.support_names if recorder else [],
+        "joints": recorder.joint_names if recorder else [],
+        "mass_kg": recorder.mass_kg if recorder else None,
+        "warnings": recorder.warnings if recorder else [],
+    }
+
+
+def _run_parent(args_cli: argparse.Namespace) -> None:
+    """父进程：不启动仿真，只做编排与合并。每个环境交给一个子进程。"""
+    import numpy as np
+
+    from eval_kit.core.aggregation import aggregate_runs, build_upper_limit_matrix
+    from eval_kit.core.report import filter_metrics, format_aggregate_summary, format_metric_result
+    from eval_kit.core.report import format_rollout_summary, format_upper_limit_summary
+
+    profile = get_profile(args_cli.profile)
+    protocol = _apply_overrides(get_protocol(args_cli.protocol), args_cli)
+    spec = get_terrain_spec(protocol.terrain_spec)
+    if args_cli.robot_mass is not None:
+        profile = replace(profile, mass_kg=args_cli.robot_mass)
+
+    if not args_cli.checkpoint:
+        raise SystemExit(
+            "编排进程需要 --checkpoint 才能派发子进程（父进程不加载 hydra，"
+            "因此无法用 --load-run 按实验名查找）。请传入 checkpoint 路径。"
+        )
+    resume_path = os.path.abspath(os.path.expanduser(args_cli.checkpoint))
+    if not os.path.isfile(resume_path):
+        raise SystemExit(f"找不到 checkpoint：{resume_path}")
+
+    print("[eval_kit] 协议预设:", args_cli.protocol)
+    print("[eval_kit] profile   :", profile.name)
+    print("[eval_kit] checkpoint:", resume_path)
+    print("[eval_kit] 口径      :", json.dumps(protocol.to_dict(), ensure_ascii=False))
+    print("[eval_kit] 地形规格  :", spec.to_dict())
+    print("[eval_kit] 种子      :", list(protocol.seeds), "（每个种子一个子进程）")
+
+    result: dict[str, object] = {
+        "suite": "eval_kit",
+        "task": args_cli.task,
+        "eval_type": args_cli.eval_type,
+        "checkpoint": resume_path,
+        "profile": profile.to_dict(),
+        "protocol": protocol.to_dict(),
+        "terrain_spec": spec.to_dict(),
+        "seeds": list(protocol.seeds),
+        "runner": "one-env-per-process",
+    }
+
+    wants_upper = args_cli.eval_type in ("upper_limits", "all")
+
+    if wants_upper:
+        terrain_types = _terrain_types_for(args_cli, spec)
+        per_seed: list[dict[str, object]] = []
+        pooled: dict[str, list[float]] = {"success": [], "max_progress": [], "max_tilt": []}
+        pooled_levels: list[np.ndarray] = []
+        pooled_types: list[np.ndarray] = []
+        recorder_report: dict[str, object] = {}
+
+        for seed in protocol.seeds:
+            data = _spawn_stage("upper", seed)
+            recorder_report = data["recorder"]  # type: ignore[assignment]
+            per_env = data["per_env"]  # type: ignore[assignment]
+            level_ids = np.asarray(data["level_ids"])  # type: ignore[arg-type]
+            type_ids = np.asarray(data["type_ids"])  # type: ignore[arg-type]
+            for key in pooled:
+                pooled[key].extend(per_env[key])  # type: ignore[index]
+            pooled_levels.append(level_ids)
+            pooled_types.append(type_ids)
+            matrix = build_upper_limit_matrix(
+                per_env, level_ids, type_ids, terrain_types, spec, protocol.num_levels  # type: ignore[arg-type]
+            )
+            per_seed.append(
+                {
+                    "seed": seed,
+                    "scores": matrix["scores"],
+                    "matrix": matrix["matrix"],
+                    "metrics": data["metrics"],
+                    "early_resets": data["early_resets"],
+                }
+            )
+            if args_cli.verbose_terrain:
+                print(f"[eval_kit] seed {seed}: " + "  ".join(f"{k}={v:.2f}" for k, v in matrix["scores"].items()))
+
+        pooled_matrix = build_upper_limit_matrix(
+            pooled, np.concatenate(pooled_levels), np.concatenate(pooled_types), terrain_types, spec, protocol.num_levels
+        )
+        result["terrain_types"] = terrain_types
+        result["upper_limits"] = pooled_matrix
+        result["upper_limits_per_seed"] = per_seed
+        result["recorder"] = recorder_report
+        print()
+        print(format_upper_limit_summary(pooled_matrix))
+        first_metrics = per_seed[0]["metrics"]  # type: ignore[index]
+        print()
+        print(format_rollout_summary(first_metrics))  # type: ignore[arg-type]
+
+        for entry in per_seed:
+            early = entry["early_resets"]  # type: ignore[index]
+            if early and early["polluted"]:  # type: ignore[index]
+                print()
+                print(_format_early_reset_warning(early, protocol, protocol.minimum_terrain_size_for_bounds()))  # type: ignore[index,arg-type]
+                break
+
+        if args_cli.eval_type == "all" and args_cli.include_summary:
+            runs = []
+            for seed in protocol.seeds:
+                data = _spawn_stage("plain", seed)
+                runs.append((seed, data["metrics"]))  # type: ignore[arg-type]
+            result["summary"] = aggregate_runs(runs)  # type: ignore[arg-type]
+            print()
+            print("[混合地形汇总]")
+            print(format_aggregate_summary(result["summary"]))  # type: ignore[arg-type]
+            if runs:
+                result["summary_metrics"] = filter_metrics(runs[0][1], args_cli.eval_type)
+    else:
+        runs = []
+        recorder_report = {}
+        early_any = None
+        for seed in protocol.seeds:
+            data = _spawn_stage("plain", seed)
+            runs.append((seed, data["metrics"]))  # type: ignore[arg-type]
+            recorder_report = data["recorder"]  # type: ignore[assignment]
+            early_any = data["early_resets"]
+        aggregate = aggregate_runs(runs)  # type: ignore[arg-type]
+        result["aggregate"] = aggregate
+        result["metrics"] = filter_metrics(runs[0][1], args_cli.eval_type)
+        result["recorder"] = recorder_report
+        result["early_resets"] = early_any
+        print()
+        print(format_metric_result(args_cli.eval_type, runs[0][1]))
+        if len(runs) > 1:
+            print()
+            print(format_aggregate_summary(aggregate))
+        if early_any and early_any["polluted"]:  # type: ignore[index]
+            print()
+            print(_format_early_reset_warning(early_any, protocol, protocol.minimum_terrain_size_for_bounds()))  # type: ignore[index,arg-type]
+
+    warnings = result.get("recorder", {}).get("warnings")  # type: ignore[union-attr]
+    if warnings:
+        print("\n[形态解析警告]")
+        for warning in warnings:  # type: ignore[union-attr]
+            print("  -", warning)
+
+    output_path = _resolve_output_path(args_cli, resume_path, args_cli.eval_type)
+    with open(output_path, "w", encoding="utf-8") as handle:
+        json.dump(result, handle, indent=2, ensure_ascii=False)
+    print(f"\n[eval_kit] 结果已保存：{output_path}")
+    if args_cli.print_json:
+        print(json.dumps(result, indent=2, ensure_ascii=False))
+
+
+def _run_child(
+    args_cli: argparse.Namespace,
+    *,
+    viewer_env_index: int | None,
+    free_camera: bool,
+    livestream: int,
+    pause_s: float,
+) -> None:
+    """子进程：启动仿真，只建一个环境，跑完把结果写进 ``--stage-out``。
+
+    ``viewer_env_index`` / ``free_camera`` / ``livestream`` / ``pause_s`` 都由
+    :func:`main` 在 ``AppLauncher`` 之前算好传进来：构造 ``AppLauncher`` 会改写
+    ``headless`` 并把 ``livestream`` 从 namespace 里 pop 掉，之后再读就晚了。
+    """
+    print(f"[eval_kit][child] stage={args_cli.stage} seed={args_cli.stage_seed} 开始", flush=True)
+    import numpy as np
+
+    from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg
+    from isaaclab_tasks.utils.hydra import hydra_task_config
+
+    import rl_training  # noqa: F401  触发任务注册
+    import rl_training.tasks  # noqa: F401
+
+    from eval_kit.core.aggregation import build_upper_limit_matrix
+    from eval_kit.core.metrics import compute_metrics
+    from eval_kit.isaaclab import env_setup
+    from eval_kit.isaaclab.policy import load_policy, make_env, resolve_checkpoint
+    from eval_kit.isaaclab.recorder import build_recorder, record_rollout, summarize_early_resets
+
+    profile = get_profile(args_cli.profile)
+    # 子进程只跑一个种子：种子的覆盖由父进程通过 --stage-seed 传进来
+    protocol = replace(_apply_overrides(get_protocol(args_cli.protocol), args_cli), seeds=(args_cli.stage_seed,))
+    spec = get_terrain_spec(protocol.terrain_spec)
+    if args_cli.robot_mass is not None:
+        profile = replace(profile, mass_kg=args_cli.robot_mass)
+
+    @hydra_task_config(args_cli.task, args_cli.agent)
+    def _child(env_cfg, agent_cfg: RslRlOnPolicyRunnerCfg):
+        experiment_name = args_cli.experiment_name or agent_cfg.experiment_name
+        log_root_path = os.path.abspath(os.path.join("logs", "rsl_rl", experiment_name))
+        resume_path = resolve_checkpoint(log_root_path, args_cli.load_run, args_cli.checkpoint)
+
+        cfg = copy.deepcopy(env_cfg)
+        if args_cli.stage == "upper":
+            terrain_types = _terrain_types_for(args_cli, spec)
+            env_setup.configure_upper_limit_env(
+                cfg, spec, protocol, terrain_types,
+                seed=args_cli.stage_seed, device=args_cli.device,
+                viewer_env_index=viewer_env_index, free_camera=free_camera,
+            )
+        else:
+            terrain_types = []
+            env_setup.configure_eval_env(
+                cfg, spec, protocol, num_envs=args_cli.num_envs,
+                device=args_cli.device, seed=args_cli.stage_seed,
+                viewer_env_index=viewer_env_index, free_camera=free_camera,
+            )
+
+        if viewer_env_index is not None:
+            num_envs = int(cfg.scene.num_envs)
+            if not (0 <= viewer_env_index < num_envs):
+                raise SystemExit(
+                    f"--view-env {viewer_env_index} 超出范围：本次共 {num_envs} 个环境（编号 0~{num_envs - 1}）。"
+                )
+            if terrain_types:
+                per_type = protocol.num_levels * protocol.envs_per_cell
+                type_index, remainder = divmod(viewer_env_index, per_type)
+                level_index = remainder // protocol.envs_per_cell
+                print(
+                    f"[eval_kit] 视口跟随 env {viewer_env_index} → 地形 {terrain_types[type_index]}"
+                    f"，难度等级 L{level_index + 1}（共 {num_envs} 个环境）",
+                    flush=True,
+                )
+            else:
+                print(f"[eval_kit] 视口跟随 env {viewer_env_index}（共 {num_envs} 个环境）", flush=True)
+
+        env = make_env(args_cli.task, cfg, agent_cfg.clip_actions)
+        recorder = build_recorder(env, profile)
+        try:
+            # 策略跟仿真放在同一张卡上：默认的 --device 也会覆盖 agent 配置里的
+            # device，否则在别人占着 cuda:0 的机器上会莫名其妙 OOM。
+            policy = load_policy(env, agent_cfg, resume_path, device=args_cli.device)
+            if args_cli.record_frames:
+                policy = _with_frame_capture(policy, args_cli.record_frames, int(args_cli.record_every))
+            if args_cli.stage == "upper":
+                level_ids, type_ids = env_setup.assign_upper_limit_origins(env, spec, protocol, terrain_types)
+            if free_camera:
+                _set_free_camera_overview(env)
+            if pause_s > 0:
+                _hold_before_rollout(
+                    env.unwrapped.sim,
+                    pause_s,
+                    start_height_note=(
+                        f"（本次起始高度被抬高了 {protocol.start_z_offset_m:.2f} m，"
+                        f"所以是离地悬着的）"
+                        if protocol.start_z_offset_m > 0
+                        else ""
+                    ),
+                )
+            trace = record_rollout(env, policy, protocol, profile, recorder)
+            if args_cli.record_frames:
+                _report_recorded_frames(args_cli.record_frames, int(args_cli.record_every), protocol.dt)
+            metrics = compute_metrics(
+                trace, profile, protocol, include_per_env=(args_cli.stage == "upper")
+            )
+            early = summarize_early_resets(trace)
+        finally:
+            env.close()
+
+        if args_cli.save_trace:
+            os.makedirs(args_cli.save_trace, exist_ok=True)
+            tag = "upper" if args_cli.stage == "upper" else args_cli.eval_type
+            trace.save(os.path.join(args_cli.save_trace, f"{tag}_seed{args_cli.stage_seed}.npz"))
+
+        payload: dict[str, object] = {
+            "seed": args_cli.stage_seed,
+            "metrics": metrics,
+            "early_resets": early,
+            "recorder": _recorder_report(recorder),
+        }
+        if args_cli.stage == "upper":
+            payload["per_env"] = metrics.pop("_per_env")
+            payload["level_ids"] = level_ids.cpu().numpy().tolist()
+            payload["type_ids"] = type_ids.cpu().numpy().tolist()
+        with open(args_cli.stage_out, "w", encoding="utf-8") as handle:
+            json.dump(payload, handle)
+
+    _child()
+    print("[eval_kit][child] 完成", flush=True)
+
+
+def main() -> None:
+    parser = _build_parser()
+    AppLauncher.add_app_launcher_args(parser)
+    args_cli, hydra_args = parser.parse_known_args()
+
+    # 父进程只做编排：它不建环境，因此也不需要启动仿真。
+    if args_cli.stage == "":
+        if _livestream_value(args_cli) > 0 and not args_cli.headless:
+            print(
+                "[eval_kit] 注意：--livestream 会自动进入 headless 模式（推流由服务端渲染），"
+                "不需要也不应该再传 --headless。",
+                flush=True,
+            )
+        _run_parent(args_cli)
+        return
+
+    # 这三个值必须在 AppLauncher 之前定下来（见 _viewer_env_index 的说明）。
+    livestream = _livestream_value(args_cli)
+    viewer_env_index = _viewer_env_index(args_cli)
+    free_camera = bool(getattr(args_cli, "free_camera", False)) and _display_enabled(args_cli)
+    pause_s = args_cli.pause_s if args_cli.pause_s is not None else (30.0 if livestream > 0 else 0.0)
+    if args_cli.free_camera and not free_camera:
+        print(
+            "[eval_kit] 提示：--free-camera 只在有画面时（本地 GUI 或 --livestream）有效，"
+            "本次是 headless 且未推流，忽略该参数。",
+            flush=True,
+        )
+    if livestream > 0:
+        _allow_client_resize(args_cli)
+
+    # 子进程：启动仿真，跑一个环境，然后退出。
+    sys.argv = [sys.argv[0]] + hydra_args
+    simulation_app = AppLauncher(args_cli).app
+    try:
+        if livestream > 0:
+            _print_livestream_info(livestream)
+        _run_child(
+            args_cli,
+            viewer_env_index=viewer_env_index,
+            free_camera=free_camera,
+            livestream=livestream,
+            pause_s=pause_s,
+        )
+    except BaseException:
+        # 关键：不能走 simulation_app.close()。它会调用 os._exit(0)，把「子进程失败」
+        # 伪装成「成功」，并且丢掉还没 flush 的 stderr —— 于是父进程只看到退出码 0
+        # 和一个空的 JSON，真实报错（例如资产配置里的 SyntaxError）完全看不到。
+        # 先把报错刷出去，再以非零码强制退出，父进程才会把真实错误报上来。
+        import traceback
+
+        traceback.print_exc()
+        for stream in (sys.stdout, sys.stderr):
+            stream.flush()
+        os._exit(1)
+    for stream in (sys.stdout, sys.stderr):
+        stream.flush()
+    simulation_app.close()
+
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/reinforcement_learning/rsl_rl/play.py b/scripts/reinforcement_learning/rsl_rl/play.py
new file mode 100644
index 0000000..3e387c3
--- /dev/null
+++ b/scripts/reinforcement_learning/rsl_rl/play.py
@@ -0,0 +1,285 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Script to play a checkpoint if an RL agent from RSL-RL."""
+
+"""Launch Isaac Sim Simulator first."""
+
+import argparse
+import os
+import sys
+
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+PROJECT_SOURCE_PATH = os.path.join(PROJECT_ROOT, "source", "rl_training")
+if PROJECT_SOURCE_PATH not in sys.path:
+    sys.path.insert(0, PROJECT_SOURCE_PATH)
+
+from isaaclab.app import AppLauncher
+
+# local imports
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+import cli_args
+
+# add argparse arguments
+parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
+parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
+parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
+parser.add_argument(
+    "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
+)
+parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
+parser.add_argument("--task", type=str, default=None, help="Name of the task.")
+parser.add_argument(
+    "--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
+)
+parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
+parser.add_argument(
+    "--use_pretrained_checkpoint",
+    action="store_true",
+    help="Use the pre-trained checkpoint from Nucleus.",
+)
+parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
+parser.add_argument("--keyboard", action="store_true", default=False, help="Whether to use keyboard.")
+parser.add_argument(
+    "--visual-only",
+    action="store_true",
+    default=False,
+    help="Display the environment with zero actions without loading an RL checkpoint.",
+)
+# append RSL-RL cli arguments
+cli_args.add_rsl_rl_args(parser)
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli, hydra_args = parser.parse_known_args()
+# always enable cameras to record video
+if args_cli.video:
+    args_cli.enable_cameras = True
+
+# clear out sys.argv for Hydra
+sys.argv = [sys.argv[0]] + hydra_args
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import gymnasium as gym
+import time
+import torch
+
+from rsl_rl.runners import OnPolicyRunner
+
+from isaaclab.devices import Se2Keyboard, Se2KeyboardCfg
+from isaaclab.envs import (
+    DirectMARLEnv,
+    DirectMARLEnvCfg,
+    DirectRLEnvCfg,
+    ManagerBasedRLEnvCfg,
+    multi_agent_to_single_agent,
+)
+from isaaclab.managers import ObservationTermCfg as ObsTerm
+from isaaclab.utils.assets import retrieve_file_path
+from isaaclab.utils.dict import print_dict
+try:
+    from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
+except ModuleNotFoundError:
+    # Isaac Lab 2.3.x and earlier keep this helper under isaaclab_rl.
+    from isaaclab_rl.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
+from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper, export_policy_as_jit, export_policy_as_onnx
+from isaaclab_tasks.utils import get_checkpoint_path
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+import rl_training
+import rl_training.tasks  # noqa: F401
+
+from rl_utils import camera_follow
+
+print(f"[INFO] Using rl_training package from: {rl_training.__file__}")
+
+
+@hydra_task_config(args_cli.task, args_cli.agent)
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlOnPolicyRunnerCfg):
+    """Play with RSL-RL agent."""
+    task_name = args_cli.task.split(":")[-1]
+    # override configurations with non-hydra CLI arguments
+    agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
+    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else 50
+
+    # set the environment seed
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = agent_cfg.seed
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
+
+    # spawn the robot randomly in the grid (instead of their terrain levels)
+    env_cfg.scene.terrain.max_init_terrain_level = None
+    # reduce the number of terrains to save memory
+    if env_cfg.scene.terrain.terrain_generator is not None:
+        env_cfg.scene.terrain.terrain_generator.num_rows = 5
+        env_cfg.scene.terrain.terrain_generator.num_cols = 5
+        env_cfg.scene.terrain.terrain_generator.curriculum = False
+
+    # disable randomization for play
+    env_cfg.observations.policy.enable_corruption = False
+    # Remove perturbations and parameter randomization for reproducible playback.
+    for event_name in (
+        "randomize_rigid_body_material",
+        "randomize_wheel_material",
+        "randomize_support_material",
+        "randomize_rigid_body_mass",
+        "randomize_rigid_body_mass_base",
+        "randomize_rigid_body_inertia",
+        "randomize_com_positions",
+        "randomize_apply_external_force_torque",
+        "randomize_actuator_gains",
+        "randomize_push_robot",
+    ):
+        if hasattr(env_cfg.events, event_name):
+            setattr(env_cfg.events, event_name, None)
+    env_cfg.curriculum.command_levels = None
+
+    if args_cli.keyboard:
+        env_cfg.scene.num_envs = 1
+        env_cfg.terminations.time_out = None
+        env_cfg.commands.base_velocity.debug_vis = False
+        config = Se2KeyboardCfg(
+            v_x_sensitivity=env_cfg.commands.base_velocity.ranges.lin_vel_x[1]/2,
+            v_y_sensitivity=env_cfg.commands.base_velocity.ranges.lin_vel_y[1],
+            omega_z_sensitivity=env_cfg.commands.base_velocity.ranges.ang_vel_z[1],
+        )
+        controller = Se2Keyboard(config)
+        env_cfg.observations.policy.velocity_commands = ObsTerm(
+            func=lambda env: torch.tensor(controller.advance(), dtype=torch.float32).unsqueeze(0).to(env.device),
+        )
+
+    # specify directory for logging experiments
+    log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)
+    log_root_path = os.path.abspath(log_root_path)
+    if args_cli.visual_only:
+        resume_path = None
+        log_dir = os.path.join(log_root_path, "visual_only")
+        print("[INFO] Visual-only mode: no policy checkpoint will be loaded.")
+    else:
+        print(f"[INFO] Loading experiment from directory: {log_root_path}")
+        if args_cli.use_pretrained_checkpoint:
+            resume_path = get_published_pretrained_checkpoint("rsl_rl", task_name)
+            if not resume_path:
+                print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
+                return
+        elif args_cli.load_run is not None:
+            resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
+        elif args_cli.checkpoint:
+            resume_path = retrieve_file_path(args_cli.checkpoint)
+        else:
+            resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
+        log_dir = os.path.dirname(resume_path)
+
+    # create isaac environment
+    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
+
+    # convert to single-agent instance if required by the RL algorithm
+    if isinstance(env.unwrapped, DirectMARLEnv):
+        env = multi_agent_to_single_agent(env)
+
+    # wrap for video recording
+    if args_cli.video:
+        video_kwargs = {
+            "video_folder": os.path.join(log_dir, "videos", "play"),
+            "step_trigger": lambda step: step == 0,
+            "video_length": args_cli.video_length,
+            "disable_logger": True,
+        }
+        print("[INFO] Recording videos during training.")
+        print_dict(video_kwargs, nesting=4)
+        env = gym.wrappers.RecordVideo(env, **video_kwargs)
+
+    # wrap around environment for rsl-rl
+    env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
+
+    if args_cli.visual_only:
+        policy = None
+    else:
+        print(f"[INFO]: Loading model checkpoint from: {resume_path}")
+        # load previously trained model
+        ppo_runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
+        ppo_runner.load(resume_path)
+
+        # obtain the trained policy for inference
+        policy = ppo_runner.get_inference_policy(device=env.unwrapped.device)
+
+        # extract the neural network module
+        # we do this in a try-except to maintain backwards compatibility.
+        try:
+            # version 2.3 onwards
+            policy_nn = ppo_runner.alg.policy
+        except AttributeError:
+            # version 2.2 and below
+            policy_nn = ppo_runner.alg.actor_critic
+
+        # export policy to onnx/jit
+        export_model_dir = os.path.join(os.path.dirname(resume_path), "exported")
+        export_policy_as_onnx(
+            policy=policy_nn,
+            normalizer=None,
+            path=export_model_dir,
+            filename="policy.onnx",
+        )
+        export_policy_as_jit(
+            policy=policy_nn,
+            normalizer=None,
+            path=export_model_dir,
+            filename="policy.pt",
+        )
+
+    dt = env.unwrapped.step_dt
+    # print(dt, "dt")
+    # reset environment
+    
+    obs = env.get_observations()
+    
+    timestep = 0
+    # simulate environment
+    while simulation_app.is_running():
+        start_time = time.time()
+        # run everything in inference mode
+        with torch.inference_mode():
+            # agent stepping
+            if policy is None:
+                actions = torch.zeros((env.num_envs, env.num_actions), device=env.unwrapped.device)
+            else:
+                actions = policy(obs)
+
+            # env stepping
+            obs, _, _, _ = env.step(actions)
+        if args_cli.video:
+            timestep += 1
+            # Exit the play loop after recording one video
+            if timestep == args_cli.video_length:
+                break
+
+        if args_cli.keyboard:
+            camera_follow(env)
+
+        # time delay for real-time evaluation
+        sleep_time = dt - (time.time() - start_time)
+        if args_cli.real_time and sleep_time > 0:
+            time.sleep(sleep_time)
+
+    # close the simulator
+    env.close()
+
+
+if __name__ == "__main__":
+    # run the main function
+    main()
+    # close sim app
+    simulation_app.close()
diff --git a/scripts/reinforcement_learning/rsl_rl/train.py b/scripts/reinforcement_learning/rsl_rl/train.py
new file mode 100644
index 0000000..b42ed3a
--- /dev/null
+++ b/scripts/reinforcement_learning/rsl_rl/train.py
@@ -0,0 +1,214 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""Script to train RL agent with RSL-RL."""
+
+"""Launch Isaac Sim Simulator first."""
+
+import argparse
+import os
+import sys
+
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+PROJECT_SOURCE_PATH = os.path.join(PROJECT_ROOT, "source", "rl_training")
+if PROJECT_SOURCE_PATH not in sys.path:
+    sys.path.insert(0, PROJECT_SOURCE_PATH)
+
+from isaaclab.app import AppLauncher
+
+# local imports
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+import cli_args
+
+# add argparse arguments
+parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
+parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
+parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
+parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
+parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
+parser.add_argument("--task", type=str, default=None, help="Name of the task.")
+parser.add_argument(
+    "--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
+)
+parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
+parser.add_argument("--max_iterations", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument(
+    "--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
+)
+# append RSL-RL cli arguments
+cli_args.add_rsl_rl_args(parser)
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+args_cli, hydra_args = parser.parse_known_args()
+
+# always enable cameras to record video
+if args_cli.video:
+    args_cli.enable_cameras = True
+
+# clear out sys.argv for Hydra
+sys.argv = [sys.argv[0]] + hydra_args
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+"""Rest everything follows."""
+
+import gymnasium as gym
+import os
+import torch
+from datetime import datetime
+
+from rsl_rl.runners import OnPolicyRunner
+
+from isaaclab.envs import (
+    DirectMARLEnv,
+    DirectMARLEnvCfg,
+    DirectRLEnvCfg,
+    ManagerBasedRLEnvCfg,
+    multi_agent_to_single_agent,
+)
+from isaaclab.utils.dict import print_dict
+from isaaclab.utils.io import dump_yaml
+from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper
+from isaaclab_tasks.utils import get_checkpoint_path
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+import rl_training
+import rl_training.tasks  # noqa: F401
+
+print(f"[INFO] Using rl_training package from: {rl_training.__file__}")
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+torch.backends.cudnn.deterministic = False
+torch.backends.cudnn.benchmark = False
+
+
+def resolve_resume_path(log_root_path: str, load_run: str | None, load_checkpoint: str | None) -> str:
+    """Resolve a resume checkpoint, allowing explicit paths across experiment folders."""
+    if load_checkpoint is not None:
+        checkpoint_path = os.path.expanduser(load_checkpoint)
+        if os.path.isabs(checkpoint_path) or os.path.dirname(checkpoint_path):
+            checkpoint_path = os.path.abspath(checkpoint_path)
+            if not os.path.isfile(checkpoint_path):
+                raise FileNotFoundError(f"Unable to find the checkpoint file: {checkpoint_path}")
+            return checkpoint_path
+
+    if load_run is not None:
+        run_path = os.path.expanduser(load_run)
+        if os.path.isabs(run_path) or os.path.dirname(run_path):
+            run_path = os.path.abspath(run_path)
+            if not os.path.isdir(run_path):
+                raise FileNotFoundError(f"Unable to find the run directory: {run_path}")
+            return get_checkpoint_path(os.path.dirname(run_path), os.path.basename(run_path), load_checkpoint)
+
+    return get_checkpoint_path(log_root_path, load_run, load_checkpoint)
+
+
+@hydra_task_config(args_cli.task, args_cli.agent)
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlOnPolicyRunnerCfg):
+    """Train with RSL-RL agent."""
+    # override configurations with non-hydra CLI arguments
+    agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
+    env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
+    agent_cfg.max_iterations = (
+        args_cli.max_iterations if args_cli.max_iterations is not None else agent_cfg.max_iterations
+    )
+
+    # set the environment seed
+    # note: certain randomizations occur in the environment initialization so we set the seed here
+    env_cfg.seed = agent_cfg.seed
+    env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
+
+    for reward_name in (
+        "track_diff_wheel_yaw_exp",
+        "lin_vel_xy_yaw_command_l2",
+        "hipx_joint_pos_penalty",
+        "hipy_joint_pos_penalty",
+    ):
+        reward_term = getattr(env_cfg.rewards, reward_name, None)
+        reward_weight = None if reward_term is None else reward_term.weight
+        print(f"[INFO] Reward term {reward_name}: weight={reward_weight}")
+
+    # multi-gpu training configuration
+    if args_cli.distributed:
+        env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
+        agent_cfg.device = f"cuda:{app_launcher.local_rank}"
+
+        # set seed to have diversity in different threads
+        seed = agent_cfg.seed + app_launcher.local_rank
+        env_cfg.seed = seed
+        agent_cfg.seed = seed
+
+    # specify directory for logging experiments
+    log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)
+    log_root_path = os.path.abspath(log_root_path)
+    print(f"[INFO] Logging experiment in directory: {log_root_path}")
+    # specify directory for logging runs: {time-stamp}_{run_name}
+    log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    # The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
+    print(f"Exact experiment name requested from command line: {log_dir}")
+    if agent_cfg.run_name:
+        log_dir += f"_{agent_cfg.run_name}"
+    log_dir = os.path.join(log_root_path, log_dir)
+
+    # create isaac environment
+    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
+
+    # convert to single-agent instance if required by the RL algorithm
+    if isinstance(env.unwrapped, DirectMARLEnv):
+        env = multi_agent_to_single_agent(env)
+
+    # save resume path before creating a new log_dir
+    if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
+        resume_path = resolve_resume_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
+
+    # wrap for video recording
+    if args_cli.video:
+        video_kwargs = {
+            "video_folder": os.path.join(log_dir, "videos", "train"),
+            "step_trigger": lambda step: step % args_cli.video_interval == 0,
+            "video_length": args_cli.video_length,
+            "disable_logger": True,
+        }
+        print("[INFO] Recording videos during training.")
+        print_dict(video_kwargs, nesting=4)
+        env = gym.wrappers.RecordVideo(env, **video_kwargs)
+
+    # wrap around environment for rsl-rl
+    env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
+
+    # create runner from rsl-rl
+    runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=log_dir, device=agent_cfg.device)
+    # write git state to logs
+    runner.add_git_repo_to_log(__file__)
+    # load the checkpoint
+    if agent_cfg.resume or agent_cfg.algorithm.class_name == "Distillation":
+        print(f"[INFO]: Loading model checkpoint from: {resume_path}")
+        # load previously trained model
+        runner.load(resume_path)
+
+    # dump the configuration into log-directory
+    dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
+    dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
+    # run training
+    runner.learn(num_learning_iterations=agent_cfg.max_iterations, init_at_random_ep_len=True)
+
+    # close the simulator
+    env.close()
+
+
+if __name__ == "__main__":
+    # run the main function
+    main()
+    # close sim app
+    simulation_app.close()
diff --git a/scripts/tools/generate_solenopsis_cad_meshes.py b/scripts/tools/generate_solenopsis_cad_meshes.py
new file mode 100644
index 0000000..9d10e06
--- /dev/null
+++ b/scripts/tools/generate_solenopsis_cad_meshes.py
@@ -0,0 +1,306 @@
+#!/usr/bin/env python3
+"""Generate rigid-link STL meshes for the Solenopsis (fire-ant) URDF.
+
+The SolidWorks STEP export uses millimetres and this script intentionally keeps
+the STL coordinates in millimetres.  The URDF applies a 0.001 mesh scale.
+
+Rigid grouping used here:
+  * base: root-level fixed parts plus each P65 fixed part and motor plate;
+  * knee: arm, P65 rotating part, W190 harness and module fasteners;
+  * wheel: W190 motor/wheel appearance assembly.
+
+The grouping is explicit so it can be audited when the CAD assembly changes.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+
+from OCP.BRep import BRep_Builder
+from OCP.BRepBuilderAPI import BRepBuilderAPI_Transform
+from OCP.BRepMesh import BRepMesh_IncrementalMesh
+from OCP.STEPCAFControl import STEPCAFControl_Reader
+from OCP.StlAPI import StlAPI_Writer
+from OCP.TCollection import TCollection_ExtendedString
+from OCP.TDataStd import TDataStd_Name
+from OCP.TDF import TDF_Label
+from OCP.TDocStd import TDocStd_Document
+from OCP.TopoDS import TopoDS_Compound
+from OCP.XCAFApp import XCAFApp_Application
+from OCP.XCAFDoc import XCAFDoc_DocumentTool, XCAFDoc_ShapeTool
+from OCP.collections import Sequence_TDF_Label
+from OCP.gp import gp_Trsf
+
+
+# SolidWorks root axes -> ROS/URDF axes: (x, y, z)_urdf = (z, x, y)_sw.
+SW_TO_URDF = (
+    (0.0, 0.0, 1.0, 0.0),
+    (1.0, 0.0, 0.0, 0.0),
+    (0.0, 1.0, 0.0, 0.0),
+    (0.0, 0.0, 0.0, 1.0),
+)
+
+IDENTITY = (
+    (1.0, 0.0, 0.0, 0.0),
+    (0.0, 1.0, 0.0, 0.0),
+    (0.0, 0.0, 1.0, 0.0),
+    (0.0, 0.0, 0.0, 1.0),
+)
+
+# Standardised URDF moving-link frames.  In both cases the arm points along
+# link -Z at q=0 and the URDF joint axis is link -Y, matching the established
+# controller convention.  The right-hand transform mirrors the CAD module in
+# the physically correct way while remaining a proper rotation (determinant 1).
+MODULE_TO_LEFT_LINK = (
+    (0.0, 0.0, -1.0, 0.0),
+    (-1.0, 0.0, 0.0, 0.0),
+    (0.0, 1.0, 0.0, 0.0),
+    (0.0, 0.0, 0.0, 1.0),
+)
+MODULE_TO_RIGHT_LINK = (
+    (0.0, 0.0, 1.0, 0.0),
+    (1.0, 0.0, 0.0, 0.0),
+    (0.0, 1.0, 0.0, 0.0),
+    (0.0, 0.0, 0.0, 1.0),
+)
+
+# The two named components are fixed to the chassis side of the P65 joint.
+BASE_COMPONENT_MARKERS = (
+    "Solenopsis-02-002 关节电机固定板",
+    "P65MAX-S外观模型-A0-20260205-固定组件",
+)
+WHEEL_COMPONENT_MARKER = "W190-S 电机外观模型"
+
+
+def matmul(a, b):
+    return tuple(
+        tuple(sum(a[i][k] * b[k][j] for k in range(4)) for j in range(4))
+        for i in range(4)
+    )
+
+
+def transform_point(t, p):
+    q = (*p, 1.0)
+    return tuple(sum(t[i][k] * q[k] for k in range(4)) for i in range(3))
+
+
+def translation(x, y, z):
+    return (
+        (1.0, 0.0, 0.0, x),
+        (0.0, 1.0, 0.0, y),
+        (0.0, 0.0, 1.0, z),
+        (0.0, 0.0, 0.0, 1.0),
+    )
+
+
+def location_matrix(label):
+    trsf = XCAFDoc_ShapeTool.GetLocation_s(label).Transformation()
+    return tuple(
+        tuple(trsf.Value(i, j) for j in range(1, 5))
+        if i < 4
+        else (0.0, 0.0, 0.0, 1.0)
+        for i in range(1, 5)
+    )
+
+
+def gp_transform(matrix):
+    t = gp_Trsf()
+    t.SetValues(*(matrix[i][j] for i in range(3) for j in range(4)))
+    return t
+
+
+class CadDocument:
+    def __init__(self, step_path: Path):
+        self.document = TDocStd_Document(TCollection_ExtendedString("solenopsis"))
+        application = XCAFApp_Application.GetApplication_s()
+        application.NewDocument(TCollection_ExtendedString("MDTV-XCAF"), self.document)
+
+        reader = STEPCAFControl_Reader()
+        reader.SetNameMode(True)
+        reader.SetColorMode(True)
+        reader.SetPropsMode(True)
+        status = reader.ReadFile(str(step_path))
+        if "RetDone" not in str(status):
+            raise RuntimeError(f"STEP read failed: {status}")
+        if not reader.Transfer(self.document):
+            raise RuntimeError("STEP transfer into XCAF failed")
+
+        self.shape_tool = XCAFDoc_DocumentTool.ShapeTool_s(self.document.Main())
+        free = Sequence_TDF_Label()
+        self.shape_tool.GetFreeShapes(free)
+        if free.Length() != 1:
+            raise RuntimeError(f"Expected one root assembly, found {free.Length()}")
+        self.root = free.Value(1)
+
+    @staticmethod
+    def label_name(label):
+        attribute = TDataStd_Name()
+        if label.FindAttribute(TDataStd_Name.GetID_s(), attribute):
+            return attribute.Get().ToExtString()
+        return ""
+
+    @staticmethod
+    def referred(label):
+        target = TDF_Label()
+        if XCAFDoc_ShapeTool.GetReferredShape_s(label, target):
+            return target
+        return label
+
+    def referred_name(self, label):
+        return self.label_name(self.referred(label))
+
+    def components(self, label):
+        target = self.referred(label)
+        result = Sequence_TDF_Label()
+        XCAFDoc_ShapeTool.GetComponents_s(target, result, False)
+        return [result.Value(i) for i in range(1, result.Length() + 1)]
+
+    def referred_shape(self, component):
+        return XCAFDoc_ShapeTool.GetShape_s(self.referred(component))
+
+
+class ShapeGroup:
+    def __init__(self):
+        self.builder = BRep_Builder()
+        self.compound = TopoDS_Compound()
+        self.builder.MakeCompound(self.compound)
+        self.members = []
+
+    def add(self, cad: CadDocument, component, output_from_parent, member_name=None):
+        output_from_component = matmul(output_from_parent, location_matrix(component))
+        shape = cad.referred_shape(component)
+        moved = BRepBuilderAPI_Transform(
+            shape, gp_transform(output_from_component), True, False
+        ).Shape()
+        self.builder.Add(self.compound, moved)
+        self.members.append(member_name or cad.referred_name(component))
+
+    def write_stl(self, output_path: Path, linear_deflection_mm: float):
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        BRepMesh_IncrementalMesh(
+            self.compound, linear_deflection_mm, False, 0.35, True
+        ).Perform()
+        writer = StlAPI_Writer()
+        # Binary STL is lossless relative to the tessellation and is typically
+        # five to eight times smaller than the default ASCII representation.
+        writer.ASCIIMode = False
+        if not writer.Write(self.compound, str(output_path)):
+            raise RuntimeError(f"Failed to write {output_path}")
+
+
+def module_name_from_hip(hip_sw):
+    # URDF forward = SW +Z, URDF left = SW +X.
+    fore_aft = "f" if hip_sw[2] > 0.0 else "h"
+    left_right = "l" if hip_sw[0] > 0.0 else "r"
+    return fore_aft + left_right
+
+
+def rotation_rpy(matrix):
+    """Return fixed-axis URDF roll/pitch/yaw for a proper rotation matrix."""
+    pitch = math.asin(max(-1.0, min(1.0, -matrix[2][0])))
+    if abs(math.cos(pitch)) > 1.0e-10:
+        roll = math.atan2(matrix[2][1], matrix[2][2])
+        yaw = math.atan2(matrix[1][0], matrix[0][0])
+    else:
+        roll = math.atan2(-matrix[0][1], matrix[1][1])
+        yaw = 0.0
+    return roll, pitch, yaw
+
+
+def generate(step_path: Path, output_dir: Path, deflection_mm: float):
+    cad = CadDocument(step_path)
+    base = ShapeGroup()
+    knees = {}
+    wheels = {}
+    manifest = {
+        "source_step": str(step_path),
+        "stl_units": "millimetre",
+        "linear_deflection_mm": deflection_mm,
+        "axis_map": "URDF (x,y,z) = SolidWorks (z,x,y)",
+        "modules": {},
+    }
+
+    module_components = []
+    for component in cad.components(cad.root):
+        name = cad.referred_name(component)
+        if name == "Solenopsis-02 运动模组总成":
+            module_components.append(component)
+        else:
+            base.add(cad, component, SW_TO_URDF, name)
+
+    if len(module_components) != 4:
+        raise RuntimeError(f"Expected four motion modules, found {len(module_components)}")
+
+    for module in module_components:
+        root_from_module = location_matrix(module)
+        hip_sw = transform_point(root_from_module, (0.0, 300.0, 0.0))
+        leg = module_name_from_hip(hip_sw)
+        if leg in knees:
+            raise RuntimeError(f"Duplicate leg classification: {leg}")
+
+        knees[leg] = ShapeGroup()
+        wheels[leg] = ShapeGroup()
+
+        # The STEP assembly stores the four legs at four arbitrary joint
+        # angles.  A URDF needs a common neutral pose, so q=0 is defined with
+        # every 300 mm arm vertical and all established joint axes at -Y.
+        module_to_link = (
+            MODULE_TO_LEFT_LINK if leg.endswith("l") else MODULE_TO_RIGHT_LINK
+        )
+        knee_from_module = matmul(
+            module_to_link, translation(0.0, -300.0, 0.0)
+        )
+        wheel_from_module = module_to_link
+
+        for child in cad.components(module):
+            name = cad.referred_name(child)
+            if any(marker in name for marker in BASE_COMPONENT_MARKERS):
+                base.add(cad, child, matmul(SW_TO_URDF, root_from_module), f"{leg}:{name}")
+            elif WHEEL_COMPONENT_MARKER in name:
+                wheels[leg].add(cad, child, wheel_from_module, name)
+            else:
+                knees[leg].add(cad, child, knee_from_module, name)
+
+        urdf_from_module = matmul(SW_TO_URDF, root_from_module)
+        hip_urdf_mm = transform_point(SW_TO_URDF, hip_sw)
+        manifest["modules"][leg] = {
+            "hip_urdf_m": [value / 1000.0 for value in hip_urdf_mm],
+            "joint_rpy_rad": [0.0, 0.0, 0.0],
+            "joint_axis": [0.0, -1.0, 0.0],
+            "cad_assembly_rpy_rad": list(rotation_rpy(urdf_from_module)),
+            "base_members": [
+                name for name in base.members if name.startswith(f"{leg}:")
+            ],
+            "knee_members": knees[leg].members,
+            "wheel_members": wheels[leg].members,
+        }
+
+    base.write_stl(output_dir / "base_link.STL", deflection_mm)
+    for leg in ("fl", "fr", "hl", "hr"):
+        knees[leg].write_stl(output_dir / f"{leg}_knee.STL", deflection_mm)
+        wheels[leg].write_stl(output_dir / f"{leg}_wheel.STL", deflection_mm)
+
+    (output_dir / "mesh_manifest.json").write_text(
+        json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("step", type=Path)
+    parser.add_argument("output_dir", type=Path)
+    parser.add_argument(
+        "--deflection-mm",
+        type=float,
+        default=0.2,
+        help="Maximum linear tessellation deflection in millimetres (default: 0.2)",
+    )
+    args = parser.parse_args()
+    generate(args.step.resolve(), args.output_dir.resolve(), args.deflection_mm)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/tools/list_envs.py b/scripts/tools/list_envs.py
new file mode 100644
index 0000000..7061935
--- /dev/null
+++ b/scripts/tools/list_envs.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Script to print all the available environments in Isaac Lab.
+
+The script iterates over all registered environments and stores the details in a table.
+It prints the name of the environment, the entry point and the config file.
+
+All the environments are registered in the `isaaclab_tasks` extension. They start
+with `Isaac` in their name.
+"""
+
+"""Launch Isaac Sim Simulator first."""
+
+from isaaclab.app import AppLauncher
+
+# launch omniverse app
+app_launcher = AppLauncher(headless=True)
+simulation_app = app_launcher.app
+
+
+"""Rest everything follows."""
+
+import gymnasium as gym
+import textwrap
+from prettytable import PrettyTable
+
+import rl_training.tasks  # noqa: F401
+
+
+def main():
+    """Print all environments registered in `isaaclab_tasks` extension."""
+    # print all the available environments
+    table = PrettyTable(["S. No.", "Task Name", "Entry Point", "Config"])
+    table.title = "Available Environments in Isaac Lab"
+    # set alignment of table columns
+    table.align["Task Name"] = "l"
+    table.align["Entry Point"] = "l"
+    table.align["Config"] = "l"
+    table.hrules = 1
+
+    # set max width for text wrapping
+    max_width = 50
+
+    # count of environments
+    index = 0
+    # acquire all Isaac environments names
+    for task_spec in gym.registry.values():
+        if "Deeprobotics" in task_spec.id:
+            # wrap long text in each column before adding it to the table
+            task_name = textwrap.fill(task_spec.id, max_width)
+            entry_point = textwrap.fill(task_spec.entry_point, max_width)
+            config = textwrap.fill(task_spec.kwargs["env_cfg_entry_point"], max_width)
+
+            # add details to table
+            table.add_row([index + 1, task_name, entry_point, config])
+            # increment count
+            index += 1
+
+    print(table)
+
+
+if __name__ == "__main__":
+    try:
+        # run the main function
+        main()
+    except Exception as e:
+        raise e
+    finally:
+        # close the app
+        simulation_app.close()
diff --git a/scripts/tools/optimize_solenopsis_stl.py b/scripts/tools/optimize_solenopsis_stl.py
new file mode 100644
index 0000000..536088f
--- /dev/null
+++ b/scripts/tools/optimize_solenopsis_stl.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""Create lightweight visual STL meshes by deterministic vertex clustering.
+
+The source meshes are left untouched.  This optimization is intended only for
+URDF ``visual`` geometry; collision geometry and all physical properties stay
+in the URDF.  Vertices are snapped to a regular grid, so the maximum movement
+of any source vertex is bounded by ``sqrt(3) * grid_mm / 2``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+
+import numpy as np
+import trimesh
+
+
+def optimize_mesh(source: Path, destination: Path, grid_mm: float) -> dict[str, object]:
+    mesh = trimesh.load_mesh(source, process=True)
+    source_vertices = len(mesh.vertices)
+    source_faces = len(mesh.faces)
+
+    # Round each vertex to its nearest grid point.  The rounding makes the
+    # geometric error bounded and gives deterministic output across runs.
+    clustered_vertices = np.rint(mesh.vertices / grid_mm).astype(np.int64)
+    unique_grid_vertices, inverse = np.unique(clustered_vertices, axis=0, return_inverse=True)
+    faces = inverse[mesh.faces]
+
+    # Remove triangles collapsed by clustering.
+    valid = (
+        (faces[:, 0] != faces[:, 1])
+        & (faces[:, 1] != faces[:, 2])
+        & (faces[:, 2] != faces[:, 0])
+    )
+    faces = faces[valid]
+
+    # Remove duplicate triangles while preserving the winding of the first
+    # occurrence.  Sorting is used only to detect duplicates.
+    canonical_faces = np.sort(faces, axis=1)
+    _, first_indices = np.unique(canonical_faces, axis=0, return_index=True)
+    faces = faces[np.sort(first_indices)]
+
+    vertices = unique_grid_vertices.astype(np.float64) * grid_mm
+    result = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)
+    result.remove_unreferenced_vertices()
+
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    result.export(destination, file_type="stl")
+
+    return {
+        "file": source.name,
+        "source_vertices": source_vertices,
+        "source_faces": source_faces,
+        "optimized_vertices": len(result.vertices),
+        "optimized_faces": len(result.faces),
+        "source_bytes": source.stat().st_size,
+        "optimized_bytes": destination.stat().st_size,
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("source_dir", type=Path)
+    parser.add_argument("output_dir", type=Path)
+    parser.add_argument("--grid-mm", type=float, default=1.0)
+    args = parser.parse_args()
+
+    if args.grid_mm <= 0.0:
+        raise ValueError("--grid-mm must be positive")
+
+    source_dir = args.source_dir.resolve()
+    output_dir = args.output_dir.resolve()
+    records = [
+        optimize_mesh(source, output_dir / source.name, args.grid_mm)
+        for source in sorted(source_dir.glob("*.STL"))
+    ]
+    if not records:
+        raise RuntimeError(f"No STL files found in {source_dir}")
+
+    manifest = {
+        "source_dir": str(source_dir),
+        "grid_mm": args.grid_mm,
+        "maximum_vertex_displacement_mm": math.sqrt(3.0) * args.grid_mm / 2.0,
+        "source_faces": sum(int(record["source_faces"]) for record in records),
+        "optimized_faces": sum(int(record["optimized_faces"]) for record in records),
+        "source_bytes": sum(int(record["source_bytes"]) for record in records),
+        "optimized_bytes": sum(int(record["optimized_bytes"]) for record in records),
+        "meshes": records,
+    }
+    (output_dir / "optimization_manifest.json").write_text(
+        json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps(manifest, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/source/rl_training/config/extension.toml b/source/rl_training/config/extension.toml
new file mode 100644
index 0000000..23c2839
--- /dev/null
+++ b/source/rl_training/config/extension.toml
@@ -0,0 +1,35 @@
+[package]
+
+# Semantic Versioning is used: https://semver.org/
+version = "1.0.0"
+
+# Description
+category = "isaaclab"
+readme  = "README.md"
+
+title = "RL Training Repo for DeepRobotics"
+author = "Bo Peng"
+maintainer = "Bo Peng"
+description="RL Training Repo for DeepRobotic, Based on IsaacLab."
+repository = "https://github.com/DeepRoboticsLab/rl_training.git"
+keywords = ["extension", "rl_training", "isaaclab"]
+
+[dependencies]
+"isaaclab" = {}
+"isaaclab_assets" = {}
+"isaaclab_mimic" = {}
+"isaaclab_rl" = {}
+"isaaclab_tasks" = {}
+# NOTE: Add additional dependencies here
+
+[[python.module]]
+name = "rl_training"
+
+[isaaclab_settings]
+# TODO: Uncomment and list any apt dependencies here.
+#       If none, leave it commented out.
+# apt_deps = ["example_package"]
+# TODO: Uncomment and provide path to a ros_ws
+#       with rosdeps to be installed. If none,
+#       leave it commented out.
+# ros_ws = "path/from/extension_root/to/ros_ws"
diff --git a/source/rl_training/pyproject.toml b/source/rl_training/pyproject.toml
new file mode 100644
index 0000000..d90ac35
--- /dev/null
+++ b/source/rl_training/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools", "wheel", "toml"]
+build-backend = "setuptools.build_meta"
diff --git a/source/rl_training/rl_training/__init__.py b/source/rl_training/rl_training/__init__.py
new file mode 100644
index 0000000..27e3905
--- /dev/null
+++ b/source/rl_training/rl_training/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Python module serving as a project/extension template.
+"""
+
+# Register Gym environments.
+from .tasks import *
+
+# Register UI extensions.
+from .ui_extension_example import *
diff --git a/source/rl_training/rl_training/assets/__init__.py b/source/rl_training/rl_training/assets/__init__.py
new file mode 100644
index 0000000..c970e4f
--- /dev/null
+++ b/source/rl_training/rl_training/assets/__init__.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Package containing asset and sensor configurations."""
+
+import os
+import toml
+
+##
+# Configuration for different assets.
+##
+
+# Conveniences to other module directories via relative paths
+ISAACLAB_ASSETS_EXT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
+"""Path to the extension source directory."""
+
+ISAACLAB_ASSETS_DATA_DIR = os.path.join(ISAACLAB_ASSETS_EXT_DIR, "../../deep_robotics_model")
+"""Path to the extension data directory."""
+
+ISAACLAB_ASSETS_METADATA = toml.load(os.path.join(ISAACLAB_ASSETS_EXT_DIR, "config", "extension.toml"))
+"""Extension metadata dictionary parsed from the extension.toml file."""
+
+# Configure the module-level variables
+__version__ = ISAACLAB_ASSETS_METADATA["package"]["version"]
diff --git a/source/rl_training/rl_training/assets/deeprobotics.py b/source/rl_training/rl_training/assets/deeprobotics.py
new file mode 100644
index 0000000..6ab8110
--- /dev/null
+++ b/source/rl_training/rl_training/assets/deeprobotics.py
@@ -0,0 +1,125 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+import isaaclab.sim as sim_utils
+from isaaclab.actuators import DCMotorCfg, DelayedPDActuatorCfg
+from isaaclab.assets.articulation import ArticulationCfg
+
+from rl_training.assets import ISAACLAB_ASSETS_DATA_DIR
+
+DEEPROBOTICS_LITE3_CFG = ArticulationCfg(
+    spawn=sim_utils.UsdFileCfg(
+        usd_path=f"{ISAACLAB_ASSETS_DATA_DIR}/Lite3/Lite3_usd/Lite3.usd",
+        activate_contact_sensors=True,
+        rigid_props=sim_utils.RigidBodyPropertiesCfg(
+            disable_gravity=False,
+            retain_accelerations=False,
+            linear_damping=0.0,
+            angular_damping=0.0,
+            max_linear_velocity=1000.0,
+            max_angular_velocity=1000.0,
+            max_depenetration_velocity=1.0,
+        ),
+        articulation_props=sim_utils.ArticulationRootPropertiesCfg(
+            enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=1
+        ),
+    ),
+    init_state=ArticulationCfg.InitialStateCfg(
+        pos=(0.0, 0.0, 0.35),
+        joint_pos={
+
+            ".*Knee_joint": 0.0,
+        },
+        joint_vel={".*": 0.0},
+    ),
+    soft_joint_pos_limit_factor=0.99,
+    actuators={
+        # "Hip": DelayedPDActuatorCfg(
+        #     joint_names_expr=[".*_Hip[X,Y]_joint"],
+        #     effort_limit=24.0,
+        #     velocity_limit=26.2,
+        #     stiffness=30.0,
+        #     damping=1.0,
+        #     friction=0.0,
+        #     armature=0.0,
+        #     min_delay=0,
+        #     max_delay=5,
+        # ),
+        "Knee": DelayedPDActuatorCfg(
+            joint_names_expr=[".*_Knee_joint"],
+            effort_limit=36.0,
+            velocity_limit=26.2,
+            stiffness=30.0,
+            damping=1.0,
+            friction=0.0,
+            armature=0.0,
+            min_delay=0,
+            max_delay=5,
+        ),
+    },
+)
+
+DEEPROBOTICS_M20_CFG = ArticulationCfg(
+    spawn=sim_utils.UrdfFileCfg(
+        asset_path=f"{ISAACLAB_ASSETS_DATA_DIR}/M20/M20_urdf/urdf/Solenopsis.urdf",
+        usd_dir="/home/suyufei/tmp/IsaacLab_m20_wheel220",
+        fix_base=False,
+        merge_fixed_joints=True,
+        joint_drive=None,
+        activate_contact_sensors=True,
+        visual_material=sim_utils.PreviewSurfaceCfg(
+            diffuse_color=(0.42, 0.45, 0.50),
+            roughness=0.65,
+            metallic=0.15,
+        ),
+        rigid_props=sim_utils.RigidBodyPropertiesCfg(
+            disable_gravity=False,
+            retain_accelerations=False,
+            linear_damping=0.0,
+            angular_damping=0.0,
+            max_linear_velocity=1000.0,
+            max_angular_velocity=1000.0,
+            max_depenetration_velocity=1.0,
+        ),
+        articulation_props=sim_utils.ArticulationRootPropertiesCfg(
+            enabled_self_collisions=False, solver_position_iteration_count=4, solver_velocity_iteration_count=1
+        ),
+    ),
+    init_state=ArticulationCfg.InitialStateCfg(
+        pos=(0.0, 0.0, 0.32),
+        joint_pos={
+            "f[l,r]_knee_joint": 0.5,
+            "h[l,r]_knee_joint": -0.5,
+            ".*wheel_joint": 0.0,
+        },
+        joint_vel={".*": 0.0},
+    ),
+    soft_joint_pos_limit_factor=0.9,
+    actuators={
+        "joint": DelayedPDActuatorCfg(
+            joint_names_expr=[".*knee_joint"],
+            effort_limit=75.0,
+            velocity_limit=17.8024,
+            stiffness=80.0,
+            damping=2.0,
+            friction=0.0,
+            armature=0.0,
+            min_delay=0,
+            max_delay=5,
+        ),
+        "wheel": DelayedPDActuatorCfg(
+            joint_names_expr=[".*_wheel_joint"],
+            effort_limit=28.68,
+            velocity_limit=109.956,
+            stiffness=0.0,
+            damping=0.6,
+            friction=1.0,
+            armature=0.00243216,
+            min_delay=0,
+            max_delay=5,
+        ),
+    },
+)
diff --git a/source/rl_training/rl_training/tasks/__init__.py b/source/rl_training/rl_training/tasks/__init__.py
new file mode 100644
index 0000000..e13871c
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/__init__.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Package containing task implementations for various robotic environments."""
+
+import os
+import toml
+
+from isaaclab_tasks.utils import import_packages
+
+##
+# Register Gym environments.
+##
+
+
+# The blacklist is used to prevent importing configs from sub-packages
+_BLACKLIST_PKGS = ["utils"]
+# Import all configs in this package
+import_packages(__name__, _BLACKLIST_PKGS)
diff --git a/source/rl_training/rl_training/tasks/manager_based/__init__.py b/source/rl_training/rl_training/tasks/manager_based/__init__.py
new file mode 100644
index 0000000..804027a
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Config-based workflow environments.
+"""
+
+import gymnasium as gym
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/__init__.py
new file mode 100644
index 0000000..6d8a370
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/__init__.py
@@ -0,0 +1,14 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Locomotion environments for legged robots."""
+
+from .velocity import *  # noqa
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/__init__.py
new file mode 100644
index 0000000..dd54bb1
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Locomotion environments with velocity-tracking commands.
+
+These environments are based on the `legged_gym` environments provided by Rudin et al.
+
+Reference:
+    https://github.com/leggedrobotics/legged_gym
+"""
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/__init__.py
new file mode 100644
index 0000000..6a7892a
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Configurations for velocity-based locomotion environments."""
+
+# We leave this file empty since we don't want to expose any configs in this package directly.
+# We still need this file to import the "config" module in the parent package.
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/__init__.py
new file mode 100644
index 0000000..6a7892a
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Configurations for velocity-based locomotion environments."""
+
+# We leave this file empty since we don't want to expose any configs in this package directly.
+# We still need this file to import the "config" module in the parent package.
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/__init__.py
new file mode 100644
index 0000000..8bdfbf1
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/__init__.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+import gymnasium as gym
+
+from . import agents
+
+##
+# Register Gym environments.
+##
+
+gym.register(
+    id="Flat-Deeprobotics-Lite3-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.flat_env_cfg:DeeproboticsLite3FlatEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsLite3FlatPPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsLite3FlatTrainerCfg",
+    },
+)
+
+gym.register(
+    id="Rough-Deeprobotics-Lite3-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.rough_env_cfg:DeeproboticsLite3RoughEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsLite3RoughPPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsLite3RoughTrainerCfg",
+    },
+)
+
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/__init__.py
new file mode 100644
index 0000000..26c100d
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/rsl_rl_ppo_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/rsl_rl_ppo_cfg.py
new file mode 100644
index 0000000..b259309
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/agents/rsl_rl_ppo_cfg.py
@@ -0,0 +1,48 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from isaaclab.utils import configclass
+from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg
+
+
+@configclass
+class DeeproboticsLite3RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
+    num_steps_per_env = 24
+    max_iterations = 10000
+    save_interval = 100
+    experiment_name = "deeprobotics_lite3_rough"
+    empirical_normalization = False
+    clip_actions = 100
+    policy = RslRlPpoActorCriticCfg(
+        init_noise_std=1.0,
+        noise_std_type="log",
+        actor_hidden_dims=[512, 256, 128],
+        critic_hidden_dims=[512, 256, 128],
+        activation="elu",
+    )
+    algorithm = RslRlPpoAlgorithmCfg(
+        value_loss_coef=1.0,
+        use_clipped_value_loss=True,
+        clip_param=0.2,
+        entropy_coef=0.01,
+        num_learning_epochs=5,
+        num_mini_batches=4,
+        learning_rate=1.0e-3,
+        schedule="adaptive",
+        gamma=0.99,
+        lam=0.95,
+        desired_kl=0.01,
+        max_grad_norm=1.0,
+    )
+
+
+@configclass
+class DeeproboticsLite3FlatPPORunnerCfg(DeeproboticsLite3RoughPPORunnerCfg):
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.max_iterations = 10000
+        self.experiment_name = "deeprobotics_lite3_flat"
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/flat_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/flat_env_cfg.py
new file mode 100644
index 0000000..448cb82
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/flat_env_cfg.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from isaaclab.utils import configclass
+
+from .rough_env_cfg import DeeproboticsLite3RoughEnvCfg
+
+
+@configclass
+class DeeproboticsLite3FlatEnvCfg(DeeproboticsLite3RoughEnvCfg):
+    def __post_init__(self):
+        # post init of parent
+        super().__post_init__()
+
+        # override rewards
+        # self.rewards.base_height_l2.params["sensor_cfg"] = None
+        # change terrain to flat
+        self.scene.terrain.terrain_type = "plane"
+        self.scene.terrain.terrain_generator = None
+        # no height scan
+        # self.scene.height_scanner = None
+        self.observations.policy.height_scan = None
+        # self.observations.critic.height_scan = None
+        # no terrain curriculum
+        self.curriculum.terrain_levels = None
+
+        # If the weight of rewards is 0, set rewards to None
+        if self.__class__.__name__ == "DeeproboticsLite3FlatEnvCfg":
+            self.disable_zero_weight_rewards()
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/rough_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/rough_env_cfg.py
new file mode 100644
index 0000000..5841730
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/quadruped/deeprobotics_lite3/rough_env_cfg.py
@@ -0,0 +1,183 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from isaaclab.utils import configclass
+
+from rl_training.tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg
+# from isaaclab.sensors.ray_caster import GridPatternCfg
+##
+# Pre-defined configs
+##
+from rl_training.assets.deeprobotics import DEEPROBOTICS_LITE3_CFG  # isort: skip
+
+
+@configclass
+class DeeproboticsLite3RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
+    base_link_name = "TORSO"
+    foot_link_name = ".*_FOOT"
+    # fmt: off
+    joint_names = [
+        "FL_HipX_joint", "FL_HipY_joint", "FL_Knee_joint",
+        "FR_HipX_joint", "FR_HipY_joint", "FR_Knee_joint",
+        "HL_HipX_joint", "HL_HipY_joint", "HL_Knee_joint",
+        "HR_HipX_joint", "HR_HipY_joint", "HR_Knee_joint",
+    ]
+
+    link_names = [
+       'TORSO', 
+       'FL_HIP', 'FR_HIP', 'HL_HIP', 'HR_HIP', 
+       'FL_THIGH', 'FR_THIGH', 'HL_THIGH', 'HR_THIGH', 
+       'FL_SHANK', 'FR_SHANK', 'HL_SHANK', 'HR_SHANK', 
+       'FL_FOOT', 'FR_FOOT', 'HL_FOOT', 'HR_FOOT',
+    ]
+    # fmt: on
+
+    def __post_init__(self):
+        # post init of parent
+        super().__post_init__()
+
+        # ------------------------------Sence------------------------------
+        self.scene.robot = DEEPROBOTICS_LITE3_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
+        self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/" + self.base_link_name
+        self.scene.height_scanner_base.prim_path = "{ENV_REGEX_NS}/Robot/" + self.base_link_name
+        self.scene.height_scanner.pattern_cfg.resolution = 0.07 #  = GridPatternCfg(resolution=0.07, size=[1.6, 1.0]),
+
+        # ------------------------------Observations------------------------------
+        self.observations.policy.base_lin_vel = None # type: ignore
+        self.observations.policy.height_scan = None # type: ignore
+        self.observations.policy.base_ang_vel.scale = 0.25
+        self.observations.policy.joint_pos.scale = 1.0
+        self.observations.policy.joint_vel.scale = 0.05
+        self.observations.policy.joint_pos.params["asset_cfg"].joint_names = self.joint_names
+        self.observations.policy.joint_vel.params["asset_cfg"].joint_names = self.joint_names
+
+        # ------------------------------Actions------------------------------
+        # reduce action scale
+        self.actions.joint_pos.scale = {".*_HipX_joint": 0.125, "^(?!.*_HipX_joint).*": 0.25}
+        self.actions.joint_pos.clip = {".*": (-100.0, 100.0)}
+        self.actions.joint_pos.joint_names = self.joint_names
+
+        # ------------------------------Events------------------------------
+        self.events.randomize_reset_base.params = {
+            "pose_range": {
+                "x": (-1.0, 1.0),
+                "y": (-1.0, 1.0),
+                "z": (0.0, 0.0),
+                "roll": (-0.3, 0.3),
+                "pitch": (-0.3, 0.3),
+                "yaw": (-3.14, 3.14),
+            },
+            "velocity_range": {
+                "x": (-0.2, 0.2),
+                "y": (-0.2, 0.2),
+                "z": (-0.2, 0.2),
+                "roll": (-0.05, 0.05),
+                "pitch": (-0.05, 0.05),
+                "yaw": (-0.0, 0.0),
+            },
+        }
+
+
+        self.events.randomize_rigid_body_mass.params["asset_cfg"].body_names = self.link_names # [self.base_link_name]
+        self.events.randomize_rigid_body_mass_base = None
+        self.events.randomize_com_positions.params["asset_cfg"].body_names = self.base_link_name # [self.base_link_name]
+        # self.events.randomize_com_positions = None
+        self.events.randomize_apply_external_force_torque = None
+        self.events.randomize_push_robot = None
+        self.events.randomize_actuator_gains.params["asset_cfg"].joint_names = self.joint_names
+
+        # set terrain generation probability to 0 for boxes and stairs
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].proportion = 0.4
+        self.scene.terrain.terrain_generator.sub_terrains["hf_pyramid_slope"].proportion = 0.3
+        self.scene.terrain.terrain_generator.sub_terrains["hf_pyramid_slope_inv"].proportion = 0.3
+        self.scene.terrain.terrain_generator.sub_terrains["boxes"].proportion = 0.0
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs"].proportion = 0.0
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs_inv"].proportion = 0.0
+        # scale down the terrains because the robot is small
+        # self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.025, 0.1)
+        # self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_width = 0.8
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.06)
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01
+
+        # ------------------------------Rewards------------------------------
+        self.rewards.action_rate_l2.weight = -0.02 #-0.02
+        # self.rewards.smoothness_2.weight = -0.0075
+
+        self.rewards.base_height_l2.weight = -10.0
+        self.rewards.base_height_l2.params["target_height"] = 0.35
+        self.rewards.base_height_l2.params["asset_cfg"].body_names = [self.base_link_name]
+
+        self.rewards.feet_air_time.weight = 5.0 # 5.0
+        self.rewards.feet_air_time.params["threshold"] = 0.5
+        self.rewards.feet_air_time.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_air_time_variance.weight = -8.0 # -8.0
+        self.rewards.feet_air_time_variance.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_slide.weight = -0.05
+        self.rewards.feet_slide.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_slide.params["asset_cfg"].body_names = [self.foot_link_name]
+        self.rewards.stand_still.weight = -0.5 # -1.0
+        self.rewards.stand_still.params["asset_cfg"].joint_names = self.joint_names
+        self.rewards.stand_still.params["command_threshold"] = 0.1
+        self.rewards.feet_height_body.weight = -2.5 # -2.5
+        self.rewards.feet_height_body.params["target_height"] = -0.35
+        self.rewards.feet_height_body.params["asset_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_height.weight = -0.2 # -0.2
+        self.rewards.feet_height.params["asset_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_height.params["target_height"] = 0.05
+        self.rewards.contact_forces.weight = -1e-1 # -2e-2
+        self.rewards.contact_forces.params["sensor_cfg"].body_names = [self.foot_link_name]
+
+        self.rewards.lin_vel_z_l2.weight = -2.0 #-2.0
+        self.rewards.ang_vel_xy_l2.weight = -0.05 # -0.05
+
+        self.rewards.track_lin_vel_xy_exp.weight = 3.0
+        self.rewards.track_ang_vel_z_exp.weight = 1.5
+
+        self.rewards.undesired_contacts.weight = -0.5
+        self.rewards.undesired_contacts.params["sensor_cfg"].body_names = [f"^(?!.*{self.foot_link_name}).*"]
+
+        self.rewards.joint_torques_l2.weight = -2.5e-5
+        self.rewards.joint_acc_l2.weight = -1e-8
+        self.rewards.joint_deviation_l1.weight = -0.5
+        self.rewards.joint_deviation_l1.params["asset_cfg"].joint_names = [".*HipX.*"]
+        self.rewards.joint_power.weight = -2e-5
+        self.rewards.flat_orientation_l2.weight = -5.0
+
+        # add the following rewards to improve the gait
+        self.rewards.feet_gait.weight = 0.5
+        self.rewards.feet_gait.params["synced_feet_pair_names"] = [
+            ["FL_FOOT", "HR_FOOT"],
+            ["FR_FOOT", "HL_FOOT"]
+        ]
+
+        self.rewards.joint_mirror.weight = -0.05
+        self.rewards.joint_mirror.params["mirror_joints"] = [
+            ["FL_(HipX|HipY|Knee).*", "HR_(HipX|HipY|Knee).*"],
+            ["FR_(HipX|HipY|Knee).*", "HL_(HipX|HipY|Knee).*"],
+        ]
+
+        self.rewards.joint_pos_limits.weight = -5.0
+        # self.rewards.joint_pos_penalty.weight = -1.0
+        self.rewards.feet_contact_without_cmd.weight = 0.1
+        self.rewards.feet_contact_without_cmd.params["sensor_cfg"].body_names = [self.foot_link_name]
+
+
+        # If the weight of rewards is 0, set rewards to None
+        if self.__class__.__name__ == "DeeproboticsLite3RoughEnvCfg":
+            self.disable_zero_weight_rewards()
+
+        # ------------------------------Terminations------------------------------
+        self.terminations.illegal_contact = None
+        # self.terminations.bad_orientation_2 = None
+
+        # ------------------------------Curriculums------------------------------
+        # self.curriculum.command_levels.params["range_multiplier"] = (0.2, 1.0)
+        self.curriculum.command_levels = None
+
+        # ------------------------------Commands------------------------------
+        self.commands.base_velocity.ranges.lin_vel_x = (-1.5, 1.5)
+        self.commands.base_velocity.ranges.lin_vel_y = (-0.8, 0.8)
+        self.commands.base_velocity.ranges.ang_vel_z = (-0.8, 0.8)
\ No newline at end of file
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/__init__.py
new file mode 100644
index 0000000..6a7892a
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Configurations for velocity-based locomotion environments."""
+
+# We leave this file empty since we don't want to expose any configs in this package directly.
+# We still need this file to import the "config" module in the parent package.
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/__init__.py
new file mode 100644
index 0000000..5f120fd
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/__init__.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+import gymnasium as gym
+
+from . import agents
+
+##
+# Register Gym environments.
+##
+
+gym.register(
+    id="Flat-Deeprobotics-M20-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.flat_env_cfg:DeeproboticsM20FlatEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsM20FlatPPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsM20FlatTrainerCfg",
+    },
+)
+
+gym.register(
+    id="Rough-Deeprobotics-M20-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.rough_env_cfg:DeeproboticsM20RoughEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsM20RoughPPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsM20RoughTrainerCfg",
+    },
+)
+
+gym.register(
+    id="Rough-Deeprobotics-M20-Support-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.rough_env_cfg:DeeproboticsM20SupportRoughEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsM20SupportRoughPPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsM20RoughTrainerCfg",
+    },
+)
+
+gym.register(
+    id="Rough-Deeprobotics-M20-Bridge-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.bridge_env_cfg:DeeproboticsM20BridgeEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsM20BridgePPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsM20RoughTrainerCfg",
+    },
+)
+
+gym.register(
+    id="Smooth-Deeprobotics-M20-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.smooth_env_cfg:DeeproboticsM20SmoothEnvCfg",
+        "rsl_rl_cfg_entry_point": f"{agents.__name__}.rsl_rl_ppo_cfg:DeeproboticsM20SmoothPPORunnerCfg",
+        "cusrl_cfg_entry_point": f"{agents.__name__}.cusrl_ppo_cfg:DeeproboticsM20RoughTrainerCfg",
+    },
+)
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/__init__.py
new file mode 100644
index 0000000..26c100d
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/rsl_rl_ppo_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/rsl_rl_ppo_cfg.py
new file mode 100644
index 0000000..7bce2cd
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/agents/rsl_rl_ppo_cfg.py
@@ -0,0 +1,78 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from isaaclab.utils import configclass
+from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg
+
+
+@configclass
+class DeeproboticsM20RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg):
+    num_steps_per_env = 24
+    max_iterations = 20000
+    save_interval = 100
+    experiment_name = "deeprobotics_m20_rough"
+    empirical_normalization = False
+    clip_actions = 100
+    policy = RslRlPpoActorCriticCfg(
+        init_noise_std=1.0,
+        noise_std_type="log",
+        actor_hidden_dims=[512, 256, 128],
+        critic_hidden_dims=[512, 256, 128],
+        activation="elu",
+    )
+    algorithm = RslRlPpoAlgorithmCfg(
+        value_loss_coef=1.0,
+        use_clipped_value_loss=True,
+        clip_param=0.2,
+        entropy_coef=0.01,
+        num_learning_epochs=5,
+        num_mini_batches=4,
+        learning_rate=1.0e-3,
+        schedule="adaptive",
+        gamma=0.99,
+        lam=0.95,
+        desired_kl=0.01,
+        max_grad_norm=1.0,
+    )
+
+
+@configclass
+class DeeproboticsM20FlatPPORunnerCfg(DeeproboticsM20RoughPPORunnerCfg):
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.max_iterations = 5000
+        self.experiment_name = "deeprobotics_m20_flat"
+
+
+@configclass
+class DeeproboticsM20SupportRoughPPORunnerCfg(DeeproboticsM20RoughPPORunnerCfg):
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.experiment_name = "deeprobotics_m20_support"
+
+
+@configclass
+class DeeproboticsM20BridgePPORunnerCfg(DeeproboticsM20RoughPPORunnerCfg):
+    """Single-side bridge fine-tuning, forward-only command."""
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.max_iterations = 5000
+        self.experiment_name = "huoyi_bridge"
+
+
+@configclass
+class DeeproboticsM20SmoothPPORunnerCfg(DeeproboticsM20RoughPPORunnerCfg):
+    """Smooth random-undulation fine-tuning, forward-only command."""
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.max_iterations = 5000
+        self.experiment_name = "huoyi_smooth"
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/bridge_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/bridge_env_cfg.py
new file mode 100644
index 0000000..612abc7
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/bridge_env_cfg.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""Single-side bridge task: left legs on a long raised bridge, right legs on flat ground."""
+
+from isaaclab.terrains import TerrainGeneratorCfg
+from isaaclab.utils import configclass
+
+import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+
+from .rough_env_cfg import DeeproboticsM20RoughEnvCfg
+
+
+@configclass
+class DeeproboticsM20BridgeEnvCfg(DeeproboticsM20RoughEnvCfg):
+    """Stage 1 policy on a long single-side bridge, forward-only command."""
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        # ------------------------------Terrain------------------------------
+        # One long bridge cell type. The bridge runs along +x, so the robot can
+        # keep walking forward without leaving the split-level configuration.
+        self.scene.terrain.terrain_generator = TerrainGeneratorCfg(
+            size=(12.0, 4.0),
+            border_width=2.0,
+            num_rows=10,
+            num_cols=20,
+            horizontal_scale=0.1,
+            vertical_scale=0.005,
+            slope_threshold=0.75,
+            use_cache=False,
+            curriculum=True,
+            sub_terrains={
+                "single_side_bridge": mdp.SingleSideBridgeTerrainCfg(
+                    proportion=1.0,
+                    bridge_height_range=(0.06, 0.14),
+                    bridge_width=1.5,
+                )
+            },
+        )
+        self.scene.terrain.max_init_terrain_level = 0
+
+        # ------------------------------Spawn------------------------------
+        # Spawn exactly on the bridge edge: y=0 and yaw=0 put the left wheels
+        # on the bridge and the right wheels on the flat ground.
+        self.events.randomize_reset_base.params = {
+            "pose_range": {
+                "x": (-1.0, 1.0),
+                "y": (0.0, 0.0),
+                "z": (0.30, 0.30),
+                "roll": (-0.03, 0.03),
+                "pitch": (-0.03, 0.03),
+                "yaw": (0.0, 0.0),
+            },
+            "velocity_range": {
+                "x": (-0.1, 0.1),
+                "y": (0.0, 0.0),
+                "z": (0.0, 0.0),
+                "roll": (0.0, 0.0),
+                "pitch": (0.0, 0.0),
+                "yaw": (0.0, 0.0),
+            },
+        }
+
+        # ------------------------------Commands------------------------------
+        # Forward-only: no lateral and no yaw command.
+        self.commands.base_velocity.heading_command = False
+        self.commands.base_velocity.rel_standing_envs = 0.0
+        self.commands.base_velocity.rel_heading_envs = 0.0
+        self.commands.base_velocity.ranges.lin_vel_x = (0.2, 0.8)
+        self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0)
+        self.commands.base_velocity.ranges.ang_vel_z = (0.0, 0.0)
+
+        # Keep the same 33-dim policy / 8-dim action layout as the other tasks.
+        self.disable_zero_weight_rewards()
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/flat_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/flat_env_cfg.py
new file mode 100644
index 0000000..62b0c84
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/flat_env_cfg.py
@@ -0,0 +1,32 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from isaaclab.utils import configclass
+
+from .rough_env_cfg import DeeproboticsM20RoughEnvCfg
+
+
+@configclass
+class DeeproboticsM20FlatEnvCfg(DeeproboticsM20RoughEnvCfg):
+    def __post_init__(self):
+        # post init of parent
+        super().__post_init__()
+
+        # override rewards
+        self.rewards.base_height_l2.params["sensor_cfg"] = None
+        # change terrain to flat
+        self.scene.terrain.terrain_type = "plane"
+        self.scene.terrain.terrain_generator = None
+        # no height scan
+        self.scene.height_scanner = None
+        self.observations.policy.height_scan = None
+        self.observations.critic.height_scan = None
+        # no terrain curriculum
+        self.curriculum.terrain_levels = None
+
+        # If the weight of rewards is 0, set rewards to None
+        if self.__class__.__name__ == "DeeproboticsM20FlatEnvCfg":
+            self.disable_zero_weight_rewards()
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py
new file mode 100644
index 0000000..47653ce
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/rough_env_cfg.py
@@ -0,0 +1,475 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from isaaclab.managers import CurriculumTermCfg as CurrTerm
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import RewardTermCfg as RewTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.managers import TerminationTermCfg as DoneTerm
+from isaaclab.utils import configclass
+
+import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+from rl_training.tasks.manager_based.locomotion.velocity.velocity_env_cfg import (
+    ActionsCfg,
+    EventCfg,
+    LocomotionVelocityRoughEnvCfg,
+    RewardsCfg,
+)
+
+##
+# Pre-defined configs
+##
+from rl_training.assets.deeprobotics import DEEPROBOTICS_M20_CFG  # isort: skip
+
+
+@configclass
+class DeeproboticsM20ActionsCfg(ActionsCfg):
+    """Action specifications for the MDP."""
+
+    joint_pos = mdp.JointPositionActionCfg(
+        asset_name="robot", joint_names=[""], scale=0.25, use_default_offset=True, clip=None, preserve_order=True
+    )
+
+    joint_vel = mdp.JointVelocityActionCfg(
+        asset_name="robot", joint_names=[""], scale=20.0, use_default_offset=True, clip=None, preserve_order=True
+    )
+
+
+@configclass
+class DeeproboticsM20EventCfg(EventCfg):
+    """Domain randomization with tire-specific contact material."""
+
+    # Replace the inherited all-body material randomization with separate tire and support materials.
+    randomize_rigid_body_material = None
+
+    randomize_wheel_material = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=".*_wheel"),
+            "static_friction_range": (0.9, 1.4),
+            "dynamic_friction_range": (0.6, 0.9),
+            "restitution_range": (0.0, 0.05),
+            "num_buckets": 1024,
+        },
+    )
+
+    randomize_support_material = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=["base_link", ".*_knee"]),
+            "static_friction_range": (0.4, 0.8),
+            "dynamic_friction_range": (0.25, 0.4),
+            "restitution_range": (0.0, 0.05),
+            "num_buckets": 1024,
+        },
+    )
+
+
+@configclass
+class DeeproboticsM20RewardsCfg(RewardsCfg):
+    """Reward terms for the MDP."""
+
+    joint_vel_wheel_l2 = RewTerm(
+        func=mdp.joint_vel_l2, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names="")}
+    )
+
+    joint_acc_wheel_l2 = RewTerm(
+        func=mdp.joint_acc_l2, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names="")}
+    )
+
+    joint_torques_wheel_l2 = RewTerm(
+        func=mdp.joint_torques_l2, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names="")}
+    )
+
+    track_diff_wheel_yaw_exp = RewTerm(
+        func=mdp.track_diff_wheel_yaw_exp,
+        weight=1.0,
+        params={
+            "command_name": "base_velocity",
+            "std": 0.6,
+            "wheel_radius": 0.11,
+            "track_width": 0.50,
+            "left_wheel_indices": (0, 2),
+            "right_wheel_indices": (1, 3),
+            "command_threshold": 0.05,
+            "asset_cfg": SceneEntityCfg("robot", joint_names=""),
+        },
+    )
+
+    lin_vel_xy_yaw_command_l2 = RewTerm(
+        func=mdp.lin_vel_xy_yaw_command_l2,
+        weight=-0.4,
+        params={
+            "command_name": "base_velocity",
+            "yaw_command_threshold": 0.25,
+            "lin_command_threshold": 0.15,
+            "asset_cfg": SceneEntityCfg("robot"),
+        },
+    )
+
+    command_progress = RewTerm(
+        func=mdp.command_progress,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "min_command": 0.12,
+            "max_reward_speed": 0.8,
+            "asset_cfg": SceneEntityCfg("robot"),
+        },
+    )
+
+    support_contact_progress = RewTerm(
+        func=mdp.ObstacleSupportEscapeReward,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "wheel_sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "asset_cfg": SceneEntityCfg("robot"),
+            "wheel_asset_cfg": SceneEntityCfg("robot", joint_names=""),
+            "wheel_radius": 0.11,
+            "support_force_threshold": 15.0,
+            "wheel_obstacle_force_threshold": 35.0,
+            "max_contact_time": 0.45,
+            "min_command": 0.15,
+            "stall_speed_ratio": 0.35,
+            "min_stall_speed": 0.12,
+            "slip_threshold": 0.30,
+            "obstacle_memory_time": 0.60,
+            "support_window_time": 0.80,
+            "recovery_speed_ratio": 0.60,
+            "min_recovery_speed": 0.18,
+            "recovery_reward_time": 0.25,
+            "cooldown_time": 0.60,
+            "recovery_bonus": 0.25,
+            "max_reward_speed": 0.8,
+        },
+    )
+
+    support_contact_excess = RewTerm(
+        func=mdp.support_contact_excess,
+        weight=0.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "force_threshold": 120.0,
+            "max_contact_time": 0.6,
+        },
+    )
+
+    knee_inward_fold_penalty = RewTerm(
+        func=mdp.knee_inward_fold_penalty,
+        weight=0.0,
+        params={
+            "front_asset_cfg": SceneEntityCfg("robot", joint_names=""),
+            "hind_asset_cfg": SceneEntityCfg("robot", joint_names=""),
+            "max_inward_tilt": 0.0,
+        },
+    )
+
+
+@configclass
+class DeeproboticsM20RoughEnvCfg(LocomotionVelocityRoughEnvCfg):
+    """Stage 1: blind tactile locomotion without body-support rewards."""
+
+    actions: DeeproboticsM20ActionsCfg = DeeproboticsM20ActionsCfg()
+    events: DeeproboticsM20EventCfg = DeeproboticsM20EventCfg()
+    rewards: DeeproboticsM20RewardsCfg = DeeproboticsM20RewardsCfg()
+
+    base_link_name = "base_link"
+    foot_link_name = ".*_wheel"
+    support_link_names = ["base_link", ".*_knee"]
+
+    # fmt: off
+    leg_joint_names = [
+        "fl_knee_joint",
+        "fr_knee_joint",
+        "hl_knee_joint",
+        "hr_knee_joint",
+    ]
+    wheel_joint_names = [
+        "fl_wheel_joint", "fr_wheel_joint", "hl_wheel_joint", "hr_wheel_joint",
+    ]
+
+    knee_joint_names = [
+        "fl_knee_joint", "fr_knee_joint", "hl_knee_joint", "hr_knee_joint",
+    ]
+    front_knee_joint_names = ["fl_knee_joint", "fr_knee_joint"]
+    hind_knee_joint_names = ["hl_knee_joint", "hr_knee_joint"]
+    joint_names = leg_joint_names + wheel_joint_names
+    # fmt: on
+
+    def __post_init__(self):
+        # post init of parent
+        super().__post_init__()
+
+        # ------------------------------Sence------------------------------
+        self.scene.robot = DEEPROBOTICS_M20_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
+        self.scene.height_scanner.prim_path = "{ENV_REGEX_NS}/Robot/" + self.base_link_name
+        self.scene.height_scanner_base.prim_path = "{ENV_REGEX_NS}/Robot/" + self.base_link_name
+        self.scene.terrain.physics_material.restitution = 0.0
+        self.sim.physics_material = self.scene.terrain.physics_material
+
+        # ------------------------------Observations------------------------------
+        self.observations.policy.joint_pos.func = mdp.joint_pos_rel_without_wheel
+        self.observations.policy.joint_pos.params["wheel_asset_cfg"] = SceneEntityCfg(
+            "robot", joint_names=self.wheel_joint_names
+        )
+        self.observations.critic.joint_pos.func = mdp.joint_pos_rel_without_wheel
+        self.observations.critic.joint_pos.params["wheel_asset_cfg"] = SceneEntityCfg(
+            "robot", joint_names=self.wheel_joint_names
+        )
+        self.observations.policy.base_lin_vel.scale = 2.0
+        self.observations.policy.base_ang_vel.scale = 0.25
+        self.observations.policy.joint_pos.scale = 1.0
+        self.observations.policy.joint_vel.scale = 0.05
+        self.observations.policy.base_lin_vel = None
+        self.observations.policy.height_scan = None
+        self.observations.policy.joint_pos.params["asset_cfg"].joint_names = self.joint_names
+        self.observations.policy.joint_vel.params["asset_cfg"].joint_names = self.joint_names
+
+        # ------------------------------Actions------------------------------
+        # reduce action scale
+        self.actions.joint_pos.scale = 0.25
+        self.actions.joint_vel.scale = 5.0
+        self.actions.joint_pos.clip = {".*": (-100.0, 100.0)}
+        self.actions.joint_vel.clip = {".*": (-100.0, 100.0)}
+        self.actions.joint_pos.joint_names = self.leg_joint_names
+        self.actions.joint_vel.joint_names = self.wheel_joint_names
+
+        # ------------------------------Events------------------------------
+        self.events.randomize_reset_base.params = {
+            "pose_range": {
+                "x": (-1.0, 1.0),
+                "y": (-1.0, 1.0),
+                "z": (0.0, 0.0),
+                "roll": (-0.3, 0.3),
+                "pitch": (-0.3, 0.3),
+                "yaw": (-3.14, 3.14),
+            },
+            "velocity_range": {
+                "x": (-0.2, 0.2),
+                "y": (-0.2, 0.2),
+                "z": (-0.2, 0.2),
+                "roll": (-0.05, 0.05),
+                "pitch": (-0.05, 0.05),
+                "yaw": (-0.0, 0.0),
+            },
+        }
+        self.events.randomize_rigid_body_mass_base.params["asset_cfg"].body_names = [self.base_link_name]
+        self.events.randomize_rigid_body_mass.params["asset_cfg"].body_names = [
+            f"^(?!.*{self.base_link_name}).*"
+        ]
+        self.events.randomize_com_positions.params["asset_cfg"].body_names = [self.base_link_name]
+        self.events.randomize_apply_external_force_torque.params["asset_cfg"].body_names = [self.base_link_name]
+
+        self.scene.terrain.max_init_terrain_level = 2
+        self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.02, 0.14)
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs"].step_height_range = (0.03, 0.16)
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs_inv"].step_height_range = (0.03, 0.16)
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.10)
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_step = 0.01
+
+        # only randomize actuator gains for actuated joints (knees + wheels), exclude hipx/hipy completely
+        self.events.randomize_actuator_gains.params["asset_cfg"].joint_names = self.joint_names
+
+        # ------------------------------Rewards------------------------------
+        # General
+        self.rewards.is_terminated.weight = 0
+
+        # Root penalties
+        self.rewards.lin_vel_z_l2.weight = -2.0
+        self.rewards.ang_vel_xy_l2.weight = -0.05
+        self.rewards.flat_orientation_l2.weight = -0.35
+        self.rewards.roll_l2.weight = -0.45
+        self.rewards.base_height_l2.weight = -0.5
+        self.rewards.base_height_l2.params["target_height"] = 0.45
+        self.rewards.base_height_l2.params["asset_cfg"].body_names = [self.base_link_name]
+        self.rewards.body_lin_acc_l2.weight = 0
+        self.rewards.body_lin_acc_l2.params["asset_cfg"].body_names = [self.base_link_name]
+
+        # Joint penalties
+        self.rewards.joint_torques_l2.weight = -2.5e-5
+        self.rewards.joint_torques_l2.params["asset_cfg"].joint_names = self.leg_joint_names
+        self.rewards.joint_torques_wheel_l2.weight = 0
+        self.rewards.joint_torques_wheel_l2.params["asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.joint_vel_l2.weight = 0
+        self.rewards.joint_vel_l2.params["asset_cfg"].joint_names = self.leg_joint_names
+        self.rewards.joint_vel_wheel_l2.weight = 0
+        self.rewards.joint_vel_wheel_l2.params["asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.joint_acc_l2.weight = -3e-6
+        self.rewards.joint_acc_l2.params["asset_cfg"].joint_names = self.leg_joint_names
+        self.rewards.joint_acc_wheel_l2.weight = -1e-7
+        self.rewards.joint_acc_wheel_l2.params["asset_cfg"].joint_names = self.wheel_joint_names
+        # self.rewards.create_joint_deviation_l1_rewterm("joint_deviation_hip_l1", -0.2, [".*_hip_joint"])
+        self.rewards.joint_pos_limits.weight = -5.0
+        self.rewards.joint_pos_limits.params["asset_cfg"].joint_names = self.leg_joint_names
+        self.rewards.joint_vel_limits.weight = 0
+        self.rewards.joint_vel_limits.params["asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.joint_power.weight = -2e-5
+        self.rewards.joint_power.params["asset_cfg"].joint_names = self.leg_joint_names
+        self.rewards.stand_still.weight = -2.0
+        self.rewards.stand_still.params["asset_cfg"].joint_names = self.leg_joint_names
+
+        self.rewards.knee_joint_pos_penalty.weight = -0.1
+        self.rewards.knee_joint_pos_penalty.params["asset_cfg"].joint_names = self.knee_joint_names
+        # Inward fold limit: with the knee link vertical at q=0, front knees fold inward for
+        # q < 0 and hind knees for q > 0. Do not allow the legs to pass the vertical
+        # (straight-down) pose on the inward side.
+        self.rewards.knee_inward_fold_penalty.weight = -1.0
+        self.rewards.knee_inward_fold_penalty.params["front_asset_cfg"] = SceneEntityCfg(
+            "robot", joint_names=self.front_knee_joint_names
+        )
+        self.rewards.knee_inward_fold_penalty.params["hind_asset_cfg"] = SceneEntityCfg(
+            "robot", joint_names=self.hind_knee_joint_names
+        )
+        self.rewards.knee_inward_fold_penalty.params["max_inward_tilt"] = 0.0
+        self.rewards.wheel_vel_penalty.weight = 0
+        self.rewards.wheel_vel_penalty.params["sensor_cfg"].body_names = self.foot_link_name
+        self.rewards.wheel_vel_penalty.params["asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.joint_mirror.weight = 0.0
+        self.rewards.joint_mirror.params["mirror_joints"] = [
+            ["fl_knee_joint", "hr_knee_joint"],
+            ["fr_knee_joint", "hl_knee_joint"],
+        ]
+
+        # Action penalties
+        self.rewards.action_rate_l2.weight = -0.015
+
+        # Contact sensor
+        self.rewards.undesired_contacts.weight = -1.0
+        self.rewards.undesired_contacts.params["sensor_cfg"].body_names = [f"^(?!.*{self.foot_link_name}).*"]
+        self.rewards.undesired_contacts.params["threshold"] = 1.0
+        self.rewards.contact_forces.weight = -1.5e-4
+        self.rewards.contact_forces.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.support_contact_progress.weight = 0.0
+        self.rewards.support_contact_progress.params["sensor_cfg"].body_names = self.support_link_names
+        self.rewards.support_contact_progress.params["wheel_sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.support_contact_progress.params["wheel_asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.support_contact_excess.weight = 0.0
+        self.rewards.support_contact_excess.params["sensor_cfg"].body_names = self.support_link_names
+
+        # Velocity-tracking rewards
+        self.rewards.track_lin_vel_xy_exp.weight = 2.0 # 1.8
+        self.rewards.track_ang_vel_z_exp.weight = 1.5 # 1.2
+        self.rewards.track_ang_vel_z_exp.params["std"] = 0.5
+        self.rewards.track_diff_wheel_yaw_exp.weight = 1.0
+        self.rewards.track_diff_wheel_yaw_exp.params["asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.lin_vel_xy_yaw_command_l2.weight = -0.4
+
+        # Others
+        self.rewards.feet_air_time.weight = 0
+        self.rewards.feet_air_time.params["threshold"] = 0.5
+        self.rewards.feet_air_time.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_contact.weight = 0
+        self.rewards.feet_contact.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_contact_without_cmd.weight = 0.1
+        self.rewards.feet_contact_without_cmd.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_stumble.weight = 0
+        self.rewards.feet_stumble.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_slide.weight = 0
+        self.rewards.feet_slide.params["sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_slide.params["asset_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_height.weight = 0
+        self.rewards.feet_height.params["target_height"] = 0.1
+        self.rewards.feet_height.params["asset_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_height_body.weight = 0
+        self.rewards.feet_height_body.params["target_height"] = -0.4
+        self.rewards.feet_height_body.params["asset_cfg"].body_names = [self.foot_link_name]
+        self.rewards.feet_gait.weight = 0
+        self.rewards.feet_gait.params["synced_feet_pair_names"] = (("fl_wheel", "hr_wheel"), ("fr_wheel", "hl_wheel"))
+        self.rewards.upward.weight = 0.08
+
+        # If the weight of rewards is 0, set rewards to None
+        if self.__class__.__name__ == "DeeproboticsM20RoughEnvCfg":
+            self.disable_zero_weight_rewards()
+
+        # ------------------------------Terminations------------------------------
+        # self.terminations.illegal_contact.params["sensor_cfg"].body_names = [self.base_link_name]
+        self.terminations.illegal_contact = None
+        self.terminations.bad_orientation_2 = DoneTerm(
+            func=mdp.bad_orientation,
+            params={"limit_angle": 0.9, "asset_cfg": SceneEntityCfg("robot")},
+        )
+
+        # ------------------------------Curriculums------------------------------
+        # self.curriculum.command_levels.params["range_multiplier"] = (0.2, 1.0)
+        self.curriculum.command_levels = None
+
+        # ------------------------------Commands------------------------------
+        self.commands.base_velocity.debug_vis = False
+        self.commands.base_velocity.ranges.lin_vel_x = (-2.0, 2.0)
+        self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0)
+        self.commands.base_velocity.ranges.ang_vel_z = (-1.5, 1.5)
+
+
+@configclass
+class DeeproboticsM20SupportRoughEnvCfg(DeeproboticsM20RoughEnvCfg):
+    """Stage 2: resume from Stage 1 and enable brief body/knee support for obstacle crossing."""
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        # Keep the same policy/action dimensions as Stage 1 for checkpoint resume.
+        # Only rewards, command distribution, and terrain curriculum are changed here.
+        self.scene.terrain.max_init_terrain_level = 1
+        self.scene.terrain.terrain_generator.sub_terrains["boxes"].proportion = 0.25
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs"].proportion = 0.25
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs_inv"].proportion = 0.15
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].proportion = 0.15
+        self.scene.terrain.terrain_generator.sub_terrains["hf_pyramid_slope"].proportion = 0.10
+        self.scene.terrain.terrain_generator.sub_terrains["hf_pyramid_slope_inv"].proportion = 0.10
+        self.scene.terrain.terrain_generator.sub_terrains["boxes"].grid_height_range = (0.03, 0.22)
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs"].step_height_range = (0.04, 0.24)
+        self.scene.terrain.terrain_generator.sub_terrains["pyramid_stairs_inv"].step_height_range = (0.04, 0.20)
+        self.scene.terrain.terrain_generator.sub_terrains["random_rough"].noise_range = (0.01, 0.14)
+
+        # Keep privileged terrain information for the critic only. The actor remains blind/tactile.
+        self.observations.policy.height_scan = None
+        self.observations.critic.height_scan.params["sensor_cfg"] = SceneEntityCfg("height_scanner")
+        self.observations.critic.height_scan.clip = (-1.0, 1.0)
+        self.observations.critic.height_scan.scale = 1.0
+
+        # Body/knee contact is allowed only as short support during forward progress.
+        self.rewards.undesired_contacts.weight = -0.10
+        self.rewards.undesired_contacts.params["sensor_cfg"].body_names = [f"^(?!.*{self.foot_link_name}).*"]
+        self.rewards.undesired_contacts.params["threshold"] = 35.0
+        self.rewards.command_progress.weight = 0.8
+        self.rewards.support_contact_progress.weight = 0.0
+        self.rewards.support_contact_progress.params["sensor_cfg"].body_names = self.support_link_names
+        self.rewards.support_contact_progress.params["wheel_sensor_cfg"].body_names = [self.foot_link_name]
+        self.rewards.support_contact_progress.params["wheel_asset_cfg"].joint_names = self.wheel_joint_names
+        self.rewards.support_contact_excess.weight = -0.60
+        self.rewards.support_contact_excess.params["sensor_cfg"].body_names = self.support_link_names
+        self.rewards.support_contact_excess.params["force_threshold"] = 100.0
+        self.rewards.support_contact_excess.params["max_contact_time"] = 0.45
+
+        # Stage 2 should still avoid the bad solution of crawling on the body.
+        self.rewards.track_lin_vel_xy_exp.weight = 1.5
+        self.rewards.track_ang_vel_z_exp.weight = 1.0
+        self.rewards.flat_orientation_l2.weight = -0.35
+        self.rewards.roll_l2.weight = -0.45
+        self.rewards.base_height_l2.weight = -0.35
+        self.rewards.base_height_l2.params["target_height"] = 0.29
+
+        # Stage 2 first focuses on forward obstacle crossing, then expands the speed range.
+        self.commands.base_velocity.heading_command = False
+        self.commands.base_velocity.rel_standing_envs = 0.0
+        self.commands.base_velocity.rel_heading_envs = 0.0
+        self.commands.base_velocity.ranges.lin_vel_x = (0.25, 1.2)
+        self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0)
+        self.commands.base_velocity.ranges.ang_vel_z = (-0.6, 0.6)
+        self.curriculum.command_levels = CurrTerm(
+            func=mdp.command_levels_positive_x,
+            params={
+                "reward_term_name": "track_lin_vel_xy_exp",
+                "initial_max_ratio": 0.35,
+                "increment": 0.1,
+            },
+        )
+
+        self.disable_zero_weight_rewards()
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/smooth_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/smooth_env_cfg.py
new file mode 100644
index 0000000..222e0a7
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/config/wheeled/deeprobotics_m20/smooth_env_cfg.py
@@ -0,0 +1,79 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+"""Smooth random-undulation task: train both pitch and roll to stay at zero."""
+
+from isaaclab.terrains import TerrainGeneratorCfg
+from isaaclab.utils import configclass
+
+import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+
+from .rough_env_cfg import DeeproboticsM20RoughEnvCfg
+
+
+@configclass
+class DeeproboticsM20SmoothEnvCfg(DeeproboticsM20RoughEnvCfg):
+    """Stage 1 policy on a smooth, randomly undulating terrain, forward-only command."""
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        # ------------------------------Terrain------------------------------
+        # Low-frequency sinusoids only: no steps, no sharp edges.
+        self.scene.terrain.terrain_generator = TerrainGeneratorCfg(
+            size=(8.0, 8.0),
+            border_width=2.0,
+            num_rows=10,
+            num_cols=20,
+            horizontal_scale=0.05,
+            vertical_scale=0.005,
+            slope_threshold=None,
+            use_cache=False,
+            curriculum=True,
+            sub_terrains={
+                "smooth_random": mdp.SmoothRandomTerrainCfg(
+                    proportion=1.0,
+                    border_width=0.25,
+                    height_range=(0.10, 0.20),
+                    wavelength_range=(1.0, 2.2),
+                    num_waves=3,
+                )
+            },
+        )
+        self.scene.terrain.max_init_terrain_level = 9
+
+        # ------------------------------Spawn------------------------------
+        self.events.randomize_reset_base.params = {
+            "pose_range": {
+                "x": (-2.0, 2.0),
+                "y": (-2.0, 2.0),
+                "z": (0.30, 0.30),
+                "roll": (-0.05, 0.05),
+                "pitch": (-0.05, 0.05),
+                "yaw": (-3.14, 3.14),
+            },
+            "velocity_range": {
+                "x": (-0.1, 0.1),
+                "y": (0.0, 0.0),
+                "z": (0.0, 0.0),
+                "roll": (0.0, 0.0),
+                "pitch": (0.0, 0.0),
+                "yaw": (0.0, 0.0),
+            },
+        }
+
+        # Commands keep the original Stage-1 distribution inherited from
+        # DeeproboticsM20RoughEnvCfg: x in [-2, 2] m/s, y = 0, yaw in [-1.5, 1.5]
+        # rad/s, heading command enabled, 2% standing and 100% heading envs.
+
+        # ------------------------------Rewards------------------------------
+        # Split the flat-orientation penalty into true Euler pitch and roll in
+        # radians. Giving -0.4 to each keeps the total strength of the previous
+        # -0.8 flat_orientation_l2 term while letting the two axes be tuned
+        # independently.
+        self.rewards.flat_orientation_l2.weight = 0.0
+        self.rewards.pitch_l2.weight = -0.4
+        self.rewards.roll_l2.weight = -0.4
+        self.rewards.ang_vel_xy_l2.weight = -0.1
+
+        self.disable_zero_weight_rewards()
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/__init__.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/__init__.py
new file mode 100644
index 0000000..216d286
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/__init__.py
@@ -0,0 +1,22 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""This sub-module contains the functions that are specific to the locomotion environments."""
+
+from isaaclab.envs.mdp import *  # noqa: F401, F403
+from isaaclab_tasks.manager_based.locomotion.velocity.mdp import *  # noqa: F401, F403
+
+from .commands import *  # noqa: F401, F403
+from .curriculums import *  # noqa: F401, F403
+from .events import *  # noqa: F401, F403
+from .observations import *  # noqa: F401, F403
+from .rewards import *  # noqa: F401, F403
+from .terrain import *  # noqa: F401, F403
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/commands.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/commands.py
new file mode 100644
index 0000000..c94fdf6
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/commands.py
@@ -0,0 +1,130 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import torch
+from typing import TYPE_CHECKING, Sequence
+
+from isaaclab.managers import CommandTerm, CommandTermCfg
+from isaaclab.utils import configclass
+
+import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+
+if TYPE_CHECKING:
+    from isaaclab.envs import ManagerBasedEnv
+
+
+class UniformThresholdVelocityCommand(mdp.UniformVelocityCommand):
+    """Command generator that generates a velocity command in SE(2) from uniform distribution with threshold."""
+
+    cfg: mdp.UniformThresholdVelocityCommandCfg
+    """The configuration of the command generator."""
+
+    def _resample_command(self, env_ids: Sequence[int]):
+        super()._resample_command(env_ids)
+        # set small commands to zero
+        self.vel_command_b[env_ids, :2] *= (torch.norm(self.vel_command_b[env_ids, :2], dim=1) > 0.2).unsqueeze(1)
+
+
+@configclass
+class UniformThresholdVelocityCommandCfg(mdp.UniformVelocityCommandCfg):
+    """Configuration for the uniform threshold velocity command generator."""
+
+    class_type: type = UniformThresholdVelocityCommand
+
+
+class DiscreteCommandController(CommandTerm):
+    """
+    Command generator that assigns discrete commands to environments.
+
+    Commands are stored as a list of predefined integers.
+    The controller maps these commands by their indices (e.g., index 0 -> 10, index 1 -> 20).
+    """
+
+    cfg: DiscreteCommandControllerCfg
+    """Configuration for the command controller."""
+
+    def __init__(self, cfg: DiscreteCommandControllerCfg, env: ManagerBasedEnv):
+        """
+        Initialize the command controller.
+
+        Args:
+            cfg: The configuration of the command controller.
+            env: The environment object.
+        """
+        # Initialize the base class
+        super().__init__(cfg, env)
+
+        # Validate that available_commands is non-empty
+        if not self.cfg.available_commands:
+            raise ValueError("The available_commands list cannot be empty.")
+
+        # Ensure all elements are integers
+        if not all(isinstance(cmd, int) for cmd in self.cfg.available_commands):
+            raise ValueError("All elements in available_commands must be integers.")
+
+        # Store the available commands
+        self.available_commands = self.cfg.available_commands
+
+        # Create buffers to store the command
+        # -- command buffer: stores discrete action indices for each environment
+        self.command_buffer = torch.zeros(self.num_envs, dtype=torch.int32, device=self.device)
+
+        # -- current_commands: stores a snapshot of the current commands (as integers)
+        self.current_commands = [self.available_commands[0]] * self.num_envs  # Default to the first command
+
+    def __str__(self) -> str:
+        """Return a string representation of the command controller."""
+        return (
+            "DiscreteCommandController:\n"
+            f"\tNumber of environments: {self.num_envs}\n"
+            f"\tAvailable commands: {self.available_commands}\n"
+        )
+
+    """
+    Properties
+    """
+
+    @property
+    def command(self) -> torch.Tensor:
+        """Return the current command buffer. Shape is (num_envs, 1)."""
+        return self.command_buffer
+
+    """
+    Implementation specific functions.
+    """
+
+    def _update_metrics(self):
+        """Update metrics for the command controller."""
+        pass
+
+    def _resample_command(self, env_ids: Sequence[int]):
+        """Resample commands for the given environments."""
+        sampled_indices = torch.randint(
+            len(self.available_commands), (len(env_ids),), dtype=torch.int32, device=self.device
+        )
+        sampled_commands = torch.tensor(
+            [self.available_commands[idx.item()] for idx in sampled_indices], dtype=torch.int32, device=self.device
+        )
+        self.command_buffer[env_ids] = sampled_commands
+
+    def _update_command(self):
+        """Update and store the current commands."""
+        self.current_commands = self.command_buffer.tolist()
+
+
+@configclass
+class DiscreteCommandControllerCfg(CommandTermCfg):
+    """Configuration for the discrete command controller."""
+
+    class_type: type = DiscreteCommandController
+
+    available_commands: list[int] = []
+    """
+    List of available discrete commands, where each element is an integer.
+    Example: [10, 20, 30, 40, 50]
+    """
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/curriculums.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/curriculums.py
new file mode 100644
index 0000000..a5b0a8f
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/curriculums.py
@@ -0,0 +1,95 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+"""Common functions that can be used to create curriculum for the learning environment.
+
+The functions can be passed to the :class:`isaaclab.managers.CurriculumTermCfg` object to enable
+the curriculum introduced by the function.
+"""
+
+from __future__ import annotations
+
+import torch
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from isaaclab.envs import ManagerBasedRLEnv
+
+
+def command_levels_vel(
+    env: ManagerBasedRLEnv,
+    env_ids: Sequence[int],
+    reward_term_name: str,
+    range_multiplier: Sequence[float] = (0.1, 1.0),
+) -> None:
+    """command_levels_vel"""
+    base_velocity_ranges = env.command_manager.get_term("base_velocity").cfg.ranges
+    # Get original velocity ranges (ONLY ON FIRST EPISODE)
+    if env.common_step_counter == 0:
+        env._original_vel_x = torch.tensor(base_velocity_ranges.lin_vel_x, device=env.device)
+        env._original_vel_y = torch.tensor(base_velocity_ranges.lin_vel_y, device=env.device)
+        env._initial_vel_x = env._original_vel_x * range_multiplier[0]
+        env._final_vel_x = env._original_vel_x * range_multiplier[1]
+        env._initial_vel_y = env._original_vel_y * range_multiplier[0]
+        env._final_vel_y = env._original_vel_y * range_multiplier[1]
+
+        # Initialize command ranges to initial values
+        base_velocity_ranges.lin_vel_x = env._initial_vel_x.tolist()
+        base_velocity_ranges.lin_vel_y = env._initial_vel_y.tolist()
+
+    # avoid updating command curriculum at each step since the maximum command is common to all envs
+    if env.common_step_counter % env.max_episode_length == 0:
+        episode_sums = env.reward_manager._episode_sums[reward_term_name]
+        reward_term_cfg = env.reward_manager.get_term_cfg(reward_term_name)
+        delta_command = torch.tensor([-0.1, 0.1], device=env.device)
+
+        # If the tracking reward is above 80% of the maximum, increase the range of commands
+        if torch.mean(episode_sums[env_ids]) / env.max_episode_length_s > 0.8 * reward_term_cfg.weight:
+            new_vel_x = torch.tensor(base_velocity_ranges.lin_vel_x, device=env.device) + delta_command
+            new_vel_y = torch.tensor(base_velocity_ranges.lin_vel_y, device=env.device) + delta_command
+
+            # Clamp to ensure we don't exceed final ranges
+            new_vel_x = torch.clamp(new_vel_x, min=env._final_vel_x[0], max=env._final_vel_x[1])
+            new_vel_y = torch.clamp(new_vel_y, min=env._final_vel_y[0], max=env._final_vel_y[1])
+
+            # Update ranges
+            base_velocity_ranges.lin_vel_x = new_vel_x.tolist()
+            base_velocity_ranges.lin_vel_y = new_vel_y.tolist()
+
+    return torch.tensor(base_velocity_ranges.lin_vel_x[1], device=env.device)
+
+
+def command_levels_positive_x(
+    env: ManagerBasedRLEnv,
+    env_ids: Sequence[int],
+    reward_term_name: str,
+    initial_max_ratio: float = 0.35,
+    increment: float = 0.1,
+) -> torch.Tensor:
+    """Expand only the upper bound of a positive forward-speed command range.
+
+    Unlike :func:`command_levels_vel`, this curriculum preserves the configured minimum forward speed. This avoids
+    producing near-zero commands in obstacle-focused stages where the policy needs enough momentum to learn climbing.
+    """
+    base_velocity_ranges = env.command_manager.get_term("base_velocity").cfg.ranges
+    if env.common_step_counter == 0:
+        original_x = torch.tensor(base_velocity_ranges.lin_vel_x, device=env.device)
+        env._positive_x_min = original_x[0]
+        env._positive_x_max = original_x[1]
+        initial_x_max = torch.maximum(env._positive_x_min, env._positive_x_max * initial_max_ratio)
+        base_velocity_ranges.lin_vel_x = [env._positive_x_min.item(), initial_x_max.item()]
+
+    if env.common_step_counter % env.max_episode_length == 0:
+        episode_sums = env.reward_manager._episode_sums[reward_term_name]
+        reward_term_cfg = env.reward_manager.get_term_cfg(reward_term_name)
+        mean_reward = torch.mean(episode_sums[env_ids]) / env.max_episode_length_s
+        if mean_reward > 0.8 * reward_term_cfg.weight:
+            current_x_max = torch.tensor(base_velocity_ranges.lin_vel_x[1], device=env.device)
+            new_x_max = torch.clamp(current_x_max + increment, max=env._positive_x_max)
+            base_velocity_ranges.lin_vel_x = [env._positive_x_min.item(), new_x_max.item()]
+
+    return torch.tensor(base_velocity_ranges.lin_vel_x[1], device=env.device)
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/events.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/events.py
new file mode 100644
index 0000000..a307b8e
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/events.py
@@ -0,0 +1,213 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import torch
+from typing import TYPE_CHECKING, Literal
+
+import isaaclab.utils.math as math_utils
+from isaaclab.assets import Articulation, RigidObject
+from isaaclab.managers import SceneEntityCfg
+
+if TYPE_CHECKING:
+    from isaaclab.envs import ManagerBasedEnv
+
+
+def randomize_rigid_body_inertia(
+    env: ManagerBasedEnv,
+    env_ids: torch.Tensor | None,
+    asset_cfg: SceneEntityCfg,
+    inertia_distribution_params: tuple[float, float],
+    operation: Literal["add", "scale", "abs"],
+    distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform",
+):
+    """Randomize the inertia tensors of the bodies by adding, scaling, or setting random values.
+
+    This function allows randomizing only the diagonal inertia tensor components (xx, yy, zz) of the bodies.
+    The function samples random values from the given distribution parameters and adds, scales, or sets the values
+    into the physics simulation based on the operation.
+
+    .. tip::
+        This function uses CPU tensors to assign the body inertias. It is recommended to use this function
+        only during the initialization of the environment.
+    """
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject | Articulation = env.scene[asset_cfg.name]
+
+    # resolve environment ids
+    if env_ids is None:
+        env_ids = torch.arange(env.scene.num_envs, device="cpu")
+    else:
+        env_ids = env_ids.cpu()
+
+    # resolve body indices
+    if asset_cfg.body_ids == slice(None):
+        body_ids = torch.arange(asset.num_bodies, dtype=torch.int, device="cpu")
+    else:
+        body_ids = torch.tensor(asset_cfg.body_ids, dtype=torch.int, device="cpu")
+
+    # get the current inertia tensors of the bodies (num_assets, num_bodies, 9 for articulations or 9 for rigid objects)
+    inertias = asset.root_physx_view.get_inertias()
+
+    # apply randomization on default values
+    inertias[env_ids[:, None], body_ids, :] = asset.data.default_inertia[env_ids[:, None], body_ids, :].clone()
+
+    # randomize each diagonal element (xx, yy, zz -> indices 0, 4, 8)
+    for idx in [0, 4, 8]:
+        # Extract and randomize the specific diagonal element
+        randomized_inertias = _randomize_prop_by_op(
+            inertias[:, :, idx],
+            inertia_distribution_params,
+            env_ids,
+            body_ids,
+            operation,
+            distribution,
+        )
+        # Assign the randomized values back to the inertia tensor
+        inertias[env_ids[:, None], body_ids, idx] = randomized_inertias
+
+    # set the inertia tensors into the physics simulation
+    asset.root_physx_view.set_inertias(inertias, env_ids)
+
+
+def randomize_com_positions(
+    env: ManagerBasedEnv,
+    env_ids: torch.Tensor | None,
+    asset_cfg: SceneEntityCfg,
+    com_distribution_params: tuple[float, float],
+    operation: Literal["add", "scale", "abs"],
+    distribution: Literal["uniform", "log_uniform", "gaussian"] = "uniform",
+):
+    """Randomize the center of mass (COM) positions for the rigid bodies.
+
+    This function allows randomizing the COM positions of the bodies in the physics simulation. The positions can be
+    randomized by adding, scaling, or setting random values sampled from the specified distribution.
+
+    .. tip::
+        This function is intended for initialization or offline adjustments, as it modifies physics properties directly.
+
+    Args:
+        env (ManagerBasedEnv): The simulation environment.
+        env_ids (torch.Tensor | None): Specific environment indices to apply randomization, or None for all environments.
+        asset_cfg (SceneEntityCfg): The configuration for the target asset whose COM will be randomized.
+        com_distribution_params (tuple[float, float]): Parameters of the distribution (e.g., min and max for uniform).
+        operation (Literal["add", "scale", "abs"]): The operation to apply for randomization.
+        distribution (Literal["uniform", "log_uniform", "gaussian"]): The distribution to sample random values from.
+    """
+    # Extract the asset (Articulation or RigidObject)
+    asset: RigidObject | Articulation = env.scene[asset_cfg.name]
+
+    # Resolve environment indices
+    if env_ids is None:
+        env_ids = torch.arange(env.scene.num_envs, device="cpu")
+    else:
+        env_ids = env_ids.cpu()
+
+    # Resolve body indices
+    if asset_cfg.body_ids == slice(None):
+        body_ids = torch.arange(asset.num_bodies, dtype=torch.int, device="cpu")
+    else:
+        body_ids = torch.tensor(asset_cfg.body_ids, dtype=torch.int, device="cpu")
+
+    # Get the current COM offsets (num_assets, num_bodies, 3)
+    com_offsets = asset.root_physx_view.get_coms()
+
+    for dim_idx in range(3):  # Randomize x, y, z independently
+        randomized_offset = _randomize_prop_by_op(
+            com_offsets[:, :, dim_idx],
+            com_distribution_params,
+            env_ids,
+            body_ids,
+            operation,
+            distribution,
+        )
+        com_offsets[env_ids[:, None], body_ids, dim_idx] = randomized_offset[env_ids[:, None], body_ids]
+
+    # Set the randomized COM offsets into the simulation
+    asset.root_physx_view.set_coms(com_offsets, env_ids)
+
+
+"""
+Internal helper functions.
+"""
+
+
+def _randomize_prop_by_op(
+    data: torch.Tensor,
+    distribution_parameters: tuple[float | torch.Tensor, float | torch.Tensor],
+    dim_0_ids: torch.Tensor | None,
+    dim_1_ids: torch.Tensor | slice,
+    operation: Literal["add", "scale", "abs"],
+    distribution: Literal["uniform", "log_uniform", "gaussian"],
+) -> torch.Tensor:
+    """Perform data randomization based on the given operation and distribution.
+
+    Args:
+        data: The data tensor to be randomized. Shape is (dim_0, dim_1).
+        distribution_parameters: The parameters for the distribution to sample values from.
+        dim_0_ids: The indices of the first dimension to randomize.
+        dim_1_ids: The indices of the second dimension to randomize.
+        operation: The operation to perform on the data. Options: 'add', 'scale', 'abs'.
+        distribution: The distribution to sample the random values from. Options: 'uniform', 'log_uniform'.
+
+    Returns:
+        The data tensor after randomization. Shape is (dim_0, dim_1).
+
+    Raises:
+        NotImplementedError: If the operation or distribution is not supported.
+    """
+    # resolve shape
+    # -- dim 0
+    if dim_0_ids is None:
+        n_dim_0 = data.shape[0]
+        dim_0_ids = slice(None) # type: ignore
+    else:
+        n_dim_0 = len(dim_0_ids)
+        if not isinstance(dim_1_ids, slice):
+            dim_0_ids = dim_0_ids[:, None]
+    # -- dim 1
+    if isinstance(dim_1_ids, slice):
+        n_dim_1 = data.shape[1]
+    else:
+        n_dim_1 = len(dim_1_ids)
+
+    # resolve the distribution
+    if distribution == "uniform":
+        dist_fn = math_utils.sample_uniform
+    elif distribution == "log_uniform":
+        dist_fn = math_utils.sample_log_uniform
+    elif distribution == "gaussian":
+        dist_fn = math_utils.sample_gaussian
+    else:
+        raise NotImplementedError(
+            f"Unknown distribution: '{distribution}' for joint properties randomization."
+            " Please use 'uniform', 'log_uniform', 'gaussian'."
+        )
+    # perform the operation
+    if operation == "add":
+        data[dim_0_ids, dim_1_ids] += dist_fn(*distribution_parameters, (n_dim_0, n_dim_1), device=data.device)
+    elif operation == "scale":
+        data[dim_0_ids, dim_1_ids] *= dist_fn(*distribution_parameters, (n_dim_0, n_dim_1), device=data.device)
+    elif operation == "abs":
+        data[dim_0_ids, dim_1_ids] = dist_fn(*distribution_parameters, (n_dim_0, n_dim_1), device=data.device)
+    else:
+        raise NotImplementedError(
+            f"Unknown operation: '{operation}' for property randomization. Please use 'add', 'scale', or 'abs'."
+        )
+    return data
+
+
+def bad_orientation_2(
+    env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot") # type: ignore
+) -> torch.Tensor:
+    """Terminate when the asset's orientation is too far from the desired orientation limits.
+
+    This is computed by checking the angle between the projected gravity vector and the z-axis.
+    """
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    return (asset.data.projected_gravity_b[:, 2] > 0) | (asset.data.projected_gravity_b[:, :2].abs() > 0.7).any(-1)
\ No newline at end of file
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/observations.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/observations.py
new file mode 100644
index 0000000..c9d9681
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/observations.py
@@ -0,0 +1,105 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import torch
+from typing import TYPE_CHECKING
+
+from isaaclab.assets import Articulation
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.sensors import ContactSensor
+
+if TYPE_CHECKING:
+    from isaaclab.envs import ManagerBasedEnv, ManagerBasedRLEnv
+
+
+# def joint_pos_rel_without_wheel(
+#     env: ManagerBasedEnv,
+#     asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+#     wheel_asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+# ) -> torch.Tensor:
+#     """The joint positions of the asset w.r.t. the default joint positions.(Without the wheel joints)"""
+#     # extract the used quantities (to enable type-hinting)
+#     asset: Articulation = env.scene[asset_cfg.name]
+#     joint_pos_rel = asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+#     joint_pos_rel[:, wheel_asset_cfg.joint_ids] = 0
+#     return joint_pos_rel
+
+def joint_pos_rel_without_wheel(
+    env: ManagerBasedEnv,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+    wheel_asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+
+    asset: Articulation = env.scene[asset_cfg.name]
+
+    # 1. 先在“完整 joint 空间”算相对位置
+    joint_pos_rel_full = asset.data.joint_pos - asset.data.default_joint_pos
+
+    # 2. 把 wheel joint 清零（这里的 index 是全局的，合法）
+    joint_pos_rel_full[:, wheel_asset_cfg.joint_ids] = 0.0
+
+    # 3. 最后再裁剪成 policy 需要的 joint
+    joint_pos_rel = joint_pos_rel_full[:, asset_cfg.joint_ids]
+
+    return joint_pos_rel
+
+
+
+def phase(env: ManagerBasedRLEnv, cycle_time: float) -> torch.Tensor:
+    if not hasattr(env, "episode_length_buf") or env.episode_length_buf is None:
+        env.episode_length_buf = torch.zeros(env.num_envs, device=env.device, dtype=torch.long)
+    phase = env.episode_length_buf[:, None] * env.step_dt / cycle_time
+    phase_tensor = torch.cat([torch.sin(2 * torch.pi * phase), torch.cos(2 * torch.pi * phase)], dim=-1)
+    return phase_tensor
+
+
+def contact_state(
+    env: ManagerBasedRLEnv,
+    sensor_cfg: SceneEntityCfg,
+    threshold: float = 1.0,
+) -> torch.Tensor:
+    """Binary contact state for selected bodies."""
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    force_norm = torch.linalg.norm(contact_sensor.data.net_forces_w[:, sensor_cfg.body_ids, :], dim=-1)
+    return (force_norm > threshold).float()
+
+
+def contact_force_norm(
+    env: ManagerBasedRLEnv,
+    sensor_cfg: SceneEntityCfg,
+    scale: float = 100.0,
+    clip: float = 2.0,
+) -> torch.Tensor:
+    """Normalized contact force magnitude for selected bodies."""
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    force_norm = torch.linalg.norm(contact_sensor.data.net_forces_w[:, sensor_cfg.body_ids, :], dim=-1)
+    return torch.clamp(force_norm / scale, min=0.0, max=clip)
+
+
+def wheel_slip_proxy(
+    env: ManagerBasedRLEnv,
+    wheel_radius: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    """A proprioceptive slip proxy from wheel peripheral speed and base forward speed."""
+    asset: Articulation = env.scene[asset_cfg.name]
+    wheel_speed = torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids] * wheel_radius)
+    base_forward_speed = torch.abs(asset.data.root_lin_vel_b[:, 0:1])
+    return torch.clamp(torch.abs(wheel_speed - base_forward_speed), min=0.0, max=3.0)
+
+
+def applied_torque(
+    env: ManagerBasedRLEnv,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+    scale: float = 80.0,
+    clip: float = 2.0,
+) -> torch.Tensor:
+    """Normalized applied motor torques."""
+    asset: Articulation = env.scene[asset_cfg.name]
+    torque = asset.data.applied_torque[:, asset_cfg.joint_ids]
+    return torch.clamp(torque / scale, min=-clip, max=clip)
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/rewards.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/rewards.py
new file mode 100644
index 0000000..160754f
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/rewards.py
@@ -0,0 +1,1027 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import torch
+from typing import TYPE_CHECKING
+
+import isaaclab.utils.math as math_utils
+from isaaclab.assets import Articulation, RigidObject
+from isaaclab.managers import ManagerTermBase
+from isaaclab.managers import RewardTermCfg as RewTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.sensors import ContactSensor, RayCaster
+from isaaclab.utils.math import quat_apply_inverse, yaw_quat
+
+if TYPE_CHECKING:
+    from isaaclab.envs import ManagerBasedRLEnv
+
+
+def track_lin_vel_xy_exp(
+    env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+) -> torch.Tensor:
+    """Reward tracking of linear velocity commands (xy axes) using exponential kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    # compute the error
+    lin_vel_error = torch.sum(
+        torch.square(env.command_manager.get_command(command_name)[:, :2] - asset.data.root_lin_vel_b[:, :2]),
+        dim=1,
+    )
+    reward = torch.exp(-lin_vel_error / std**2)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def track_ang_vel_z_exp(
+    env: ManagerBasedRLEnv, std: float, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+) -> torch.Tensor:
+    """Reward tracking of angular velocity commands (yaw) using exponential kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    # compute the error
+    ang_vel_error = torch.square(env.command_manager.get_command(command_name)[:, 2] - asset.data.root_ang_vel_b[:, 2])
+    reward = torch.exp(-ang_vel_error / std**2)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def track_diff_wheel_yaw_exp(
+    env: ManagerBasedRLEnv,
+    std: float,
+    command_name: str,
+    wheel_radius: float,
+    track_width: float,
+    left_wheel_indices: tuple[int, ...],
+    right_wheel_indices: tuple[int, ...],
+    command_threshold: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    """Reward differential wheel speed matching the commanded yaw rate."""
+    asset: Articulation = env.scene[asset_cfg.name]
+    joint_vel = asset.data.joint_vel[:, asset_cfg.joint_ids]
+
+    left_wheel_vel = torch.mean(joint_vel[:, left_wheel_indices], dim=1)
+    right_wheel_vel = torch.mean(joint_vel[:, right_wheel_indices], dim=1)
+    estimated_yaw_rate = wheel_radius * (right_wheel_vel - left_wheel_vel) / track_width
+    yaw_command = env.command_manager.get_command(command_name)[:, 2]
+
+    yaw_error = torch.square(yaw_command - estimated_yaw_rate)
+    reward = torch.exp(-yaw_error / std**2)
+    return torch.where(torch.abs(yaw_command) > command_threshold, reward, torch.zeros_like(reward))
+
+
+def lin_vel_xy_yaw_command_l2(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    yaw_command_threshold: float,
+    lin_command_threshold: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    """Penalize linear drift when the command asks mostly for in-place yaw."""
+    asset: Articulation = env.scene[asset_cfg.name]
+    command = env.command_manager.get_command(command_name)
+    is_turn_in_place = (
+        torch.abs(command[:, 2]) > yaw_command_threshold
+    ) & (torch.linalg.norm(command[:, :2], dim=1) < lin_command_threshold)
+    lin_vel_error = torch.sum(torch.square(asset.data.root_lin_vel_b[:, :2]), dim=1)
+    return torch.where(is_turn_in_place, lin_vel_error, torch.zeros_like(lin_vel_error))
+
+
+def command_progress(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    min_command: float,
+    max_reward_speed: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    """Reward positive progress along the commanded planar direction."""
+    asset: Articulation = env.scene[asset_cfg.name]
+    command_xy = env.command_manager.get_command(command_name)[:, :2]
+    command_norm = torch.linalg.norm(command_xy, dim=1)
+    command_dir = command_xy / torch.clamp(command_norm.unsqueeze(1), min=1e-6)
+    progress_speed = torch.sum(asset.data.root_lin_vel_b[:, :2] * command_dir, dim=1)
+    reward = torch.clamp(progress_speed, min=0.0, max=max_reward_speed) / max_reward_speed
+    return torch.where(command_norm > min_command, reward, torch.zeros_like(reward))
+
+
+def support_contact_excess(
+    env: ManagerBasedRLEnv,
+    sensor_cfg: SceneEntityCfg,
+    force_threshold: float = 120.0,
+    max_contact_time: float = 0.6,
+) -> torch.Tensor:
+    """Penalize dragging or high-force support contacts."""
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    force_norm = torch.linalg.norm(contact_sensor.data.net_forces_w[:, sensor_cfg.body_ids, :], dim=-1)
+    contact_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids]
+
+    force_excess = torch.clamp(force_norm - force_threshold, min=0.0) / force_threshold
+    time_excess = torch.clamp(contact_time - max_contact_time, min=0.0)
+    return torch.sum(force_excess + time_excess, dim=1)
+
+
+class ObstacleSupportEscapeReward(ManagerTermBase):
+    """Reward a blocked -> brief support contact -> recovered progress sequence.
+
+    The state machine prevents ordinary forward motion with incidental support contact from receiving the full reward.
+    Obstacle evidence is inferred from low commanded progress together with either horizontal wheel force or wheel slip,
+    so the actor can remain blind while the training reward uses contact information already available on the robot.
+    """
+
+    def __init__(self, cfg: RewTerm, env: ManagerBasedRLEnv):
+        super().__init__(cfg, env)
+        self._blocked_memory = torch.zeros(self.num_envs, device=self.device)
+        self._support_window = torch.zeros(self.num_envs, device=self.device)
+        self._recovery_reward_window = torch.zeros(self.num_envs, device=self.device)
+        self._cooldown = torch.zeros(self.num_envs, device=self.device)
+        self._previous_support_active = torch.zeros(self.num_envs, dtype=torch.bool, device=self.device)
+
+    def reset(self, env_ids=None) -> None:
+        if env_ids is None:
+            env_ids = slice(None)
+        self._blocked_memory[env_ids] = 0.0
+        self._support_window[env_ids] = 0.0
+        self._recovery_reward_window[env_ids] = 0.0
+        self._cooldown[env_ids] = 0.0
+        self._previous_support_active[env_ids] = False
+
+    def __call__(
+        self,
+        env: ManagerBasedRLEnv,
+        command_name: str,
+        sensor_cfg: SceneEntityCfg,
+        wheel_sensor_cfg: SceneEntityCfg,
+        asset_cfg: SceneEntityCfg,
+        wheel_asset_cfg: SceneEntityCfg,
+        wheel_radius: float,
+        support_force_threshold: float,
+        wheel_obstacle_force_threshold: float,
+        max_contact_time: float,
+        min_command: float,
+        stall_speed_ratio: float,
+        min_stall_speed: float,
+        slip_threshold: float,
+        obstacle_memory_time: float,
+        support_window_time: float,
+        recovery_speed_ratio: float,
+        min_recovery_speed: float,
+        recovery_reward_time: float,
+        cooldown_time: float,
+        recovery_bonus: float,
+        max_reward_speed: float,
+    ) -> torch.Tensor:
+        asset: Articulation = env.scene[asset_cfg.name]
+        contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+        dt = float(env.step_dt)
+
+        command_x = env.command_manager.get_command(command_name)[:, 0]
+        command_speed = torch.abs(command_x)
+        direction = torch.where(command_x >= 0.0, torch.ones_like(command_x), -torch.ones_like(command_x))
+        progress_speed = direction * asset.data.root_lin_vel_b[:, 0]
+        commanded_motion = command_speed > min_command
+
+        wheel_forces = contact_sensor.data.net_forces_w[:, wheel_sensor_cfg.body_ids, :]
+        max_horizontal_wheel_force = torch.linalg.norm(wheel_forces[..., :2], dim=-1).amax(dim=1)
+        wheel_joint_vel = asset.data.joint_vel[:, wheel_asset_cfg.joint_ids]
+        mean_wheel_surface_speed = torch.mean(torch.abs(wheel_joint_vel), dim=1) * wheel_radius
+        wheel_slip = torch.clamp(mean_wheel_surface_speed - torch.clamp(progress_speed, min=0.0), min=0.0)
+
+        stall_limit = torch.maximum(stall_speed_ratio * command_speed, torch.full_like(command_speed, min_stall_speed))
+        obstacle_evidence = commanded_motion & (progress_speed < stall_limit) & (
+            (max_horizontal_wheel_force > wheel_obstacle_force_threshold) | (wheel_slip > slip_threshold)
+        )
+
+        self._blocked_memory = torch.clamp(self._blocked_memory - dt, min=0.0)
+        self._support_window = torch.clamp(self._support_window - dt, min=0.0)
+        self._recovery_reward_window = torch.clamp(self._recovery_reward_window - dt, min=0.0)
+        self._cooldown = torch.clamp(self._cooldown - dt, min=0.0)
+        self._blocked_memory = torch.where(
+            obstacle_evidence, torch.full_like(self._blocked_memory, obstacle_memory_time), self._blocked_memory
+        )
+
+        support_force = torch.linalg.norm(contact_sensor.data.net_forces_w[:, sensor_cfg.body_ids, :], dim=-1)
+        support_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids]
+        support_active = torch.any(support_force > support_force_threshold, dim=1)
+        brief_support = torch.any(
+            (support_force > support_force_threshold) & (support_time <= max_contact_time), dim=1
+        )
+        support_onset = brief_support & ~self._previous_support_active
+        valid_support_onset = support_onset & (self._blocked_memory > 0.0) & (self._cooldown <= 0.0)
+
+        was_waiting_for_recovery = self._support_window > 0.0
+        self._support_window = torch.where(
+            valid_support_onset, torch.full_like(self._support_window, support_window_time), self._support_window
+        )
+
+        recovery_threshold = torch.maximum(
+            recovery_speed_ratio * command_speed, torch.full_like(command_speed, min_recovery_speed)
+        )
+        excessive_support = torch.any(
+            (support_force > support_force_threshold) & (support_time > max_contact_time), dim=1
+        )
+        recovered = (
+            was_waiting_for_recovery
+            & commanded_motion
+            & (progress_speed >= recovery_threshold)
+            & ~excessive_support
+        )
+        self._recovery_reward_window = torch.where(
+            recovered,
+            torch.full_like(self._recovery_reward_window, recovery_reward_time),
+            self._recovery_reward_window,
+        )
+        self._support_window = torch.where(recovered, torch.zeros_like(self._support_window), self._support_window)
+        self._cooldown = torch.where(recovered, torch.full_like(self._cooldown, cooldown_time), self._cooldown)
+
+        recovery_progress = torch.clamp(progress_speed, min=0.0, max=max_reward_speed) / max_reward_speed
+        # Delay the discrete bonus until recovery is observed. This makes support contact alone worthless and
+        # prevents the policy from farming reward by repeatedly tapping the body against an obstacle.
+        reward = recovery_bonus * recovered.float() / dt
+        reward += torch.where(
+            self._recovery_reward_window > 0.0, recovery_progress, torch.zeros_like(recovery_progress)
+        )
+
+        self._previous_support_active = support_active
+        return reward
+
+
+def track_lin_vel_xy_yaw_frame_exp(
+    env, std: float, command_name: str, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+) -> torch.Tensor:
+    """Reward tracking of linear velocity commands (xy axes) in the gravity aligned robot frame using exponential kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset = env.scene[asset_cfg.name]
+    vel_yaw = quat_apply_inverse(yaw_quat(asset.data.root_quat_w), asset.data.root_lin_vel_w[:, :3])
+    lin_vel_error = torch.sum(
+        torch.square(env.command_manager.get_command(command_name)[:, :2] - vel_yaw[:, :2]), dim=1
+    )
+    reward = torch.exp(-lin_vel_error / std**2)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def track_ang_vel_z_world_exp(
+    env, command_name: str, std: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+) -> torch.Tensor:
+    """Reward tracking of angular velocity commands (yaw) in world frame using exponential kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset = env.scene[asset_cfg.name]
+    ang_vel_error = torch.square(env.command_manager.get_command(command_name)[:, 2] - asset.data.root_ang_vel_w[:, 2])
+    reward = torch.exp(-ang_vel_error / std**2)
+    reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def joint_power(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Reward joint_power"""
+    # extract the used quantities (to enable type-hinting)
+    asset: Articulation = env.scene[asset_cfg.name]
+    # compute the reward
+    reward = torch.sum(
+        torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids] * asset.data.applied_torque[:, asset_cfg.joint_ids]),
+        dim=1,
+    )
+    return reward
+
+
+def stand_still_without_cmd(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    command_threshold: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    """Penalize joint positions that deviate from the default one when no command."""
+    # extract the used quantities (to enable type-hinting)
+    asset: Articulation = env.scene[asset_cfg.name]
+    # compute out of limits constraints
+    diff_angle = asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+    reward = torch.sum(torch.abs(diff_angle), dim=1)
+    reward *= torch.linalg.norm(env.command_manager.get_command(command_name), dim=1) < command_threshold
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+def joint_pos_penalty(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    asset_cfg: SceneEntityCfg,
+    stand_still_scale: float,
+    velocity_threshold: float,
+    command_threshold: float,
+) -> torch.Tensor:
+    """Penalize joint position error from default on the articulation."""
+    # extract the used quantities (to enable type-hinting)
+    asset: Articulation = env.scene[asset_cfg.name]
+    cmd = torch.linalg.norm(env.command_manager.get_command(command_name), dim=1)
+    body_vel = torch.linalg.norm(asset.data.root_lin_vel_b[:, :2], dim=1)
+    running_reward = torch.linalg.norm(
+        (asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids]), dim=1
+    )
+    reward = torch.where(
+        torch.logical_or(cmd > command_threshold, body_vel > velocity_threshold),
+        running_reward,
+        stand_still_scale * running_reward,
+    )
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def knee_inward_fold_penalty(
+    env: ManagerBasedRLEnv,
+    front_asset_cfg: SceneEntityCfg,
+    hind_asset_cfg: SceneEntityCfg,
+    max_inward_tilt: float = 0.0,
+) -> torch.Tensor:
+    """Penalize front/hind knee joints that fold inward past ``max_inward_tilt``.
+
+    The knee link points straight down when its joint angle is zero, so the signed joint
+    position is the leg's tilt from vertical (all four knee axes are ``-Y`` in the URDF).
+    Front legs fold inward when the angle decreases, hind legs fold inward when it
+    increases. ``max_inward_tilt`` is the largest inward tilt allowed from the vertical
+    (straight-down) pose: 0.0 stops the leg at vertical, ``pi / 2`` would allow it to
+    reach horizontal. Only the inward direction is limited; outward folding is left to
+    the regular joint position limits. The penalty is quadratic in the excess angle and
+    is zero while the joint stays inside the limit.
+    """
+    front_asset: Articulation = env.scene[front_asset_cfg.name]
+    hind_asset: Articulation = env.scene[hind_asset_cfg.name]
+    front_pos = front_asset.data.joint_pos[:, front_asset_cfg.joint_ids]
+    hind_pos = hind_asset.data.joint_pos[:, hind_asset_cfg.joint_ids]
+    # front: q >= -max_inward_tilt, hind: q <= max_inward_tilt
+    front_excess = torch.clamp(-max_inward_tilt - front_pos, min=0.0)
+    hind_excess = torch.clamp(hind_pos - max_inward_tilt, min=0.0)
+    return torch.sum(front_excess.square() + hind_excess.square(), dim=1)
+
+
+def wheel_vel_penalty(
+    env: ManagerBasedRLEnv,
+    sensor_cfg: SceneEntityCfg,
+    command_name: str,
+    velocity_threshold: float,
+    command_threshold: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    asset: Articulation = env.scene[asset_cfg.name]
+    cmd = torch.linalg.norm(env.command_manager.get_command(command_name), dim=1)
+    body_vel = torch.linalg.norm(asset.data.root_lin_vel_b[:, :2], dim=1)
+    joint_vel = torch.abs(asset.data.joint_vel[:, asset_cfg.joint_ids])
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    in_air = contact_sensor.compute_first_air(env.step_dt)[:, sensor_cfg.body_ids]
+    running_reward = torch.sum(in_air * joint_vel, dim=1)
+    standing_reward = torch.sum(joint_vel, dim=1)
+    reward = torch.where(
+        torch.logical_or(cmd > command_threshold, body_vel > velocity_threshold),
+        running_reward,
+        standing_reward,
+    )
+    return reward
+
+
+class GaitReward(ManagerTermBase):
+    """Gait enforcing reward term for quadrupeds.
+
+    This reward penalizes contact timing differences between selected foot pairs defined in :attr:`synced_feet_pair_names`
+    to bias the policy towards a desired gait, i.e trotting, bounding, or pacing. Note that this reward is only for
+    quadrupedal gaits with two pairs of synchronized feet.
+    """
+
+    def __init__(self, cfg: RewTerm, env: ManagerBasedRLEnv):
+        """Initialize the term.
+
+        Args:
+            cfg: The configuration of the reward.
+            env: The RL environment instance.
+        """
+        super().__init__(cfg, env)
+        self.std: float = cfg.params["std"]
+        self.command_name: str = cfg.params["command_name"]
+        self.max_err: float = cfg.params["max_err"]
+        self.velocity_threshold: float = cfg.params["velocity_threshold"]
+        self.command_threshold: float = cfg.params["command_threshold"]
+        self.contact_sensor: ContactSensor = env.scene.sensors[cfg.params["sensor_cfg"].name]
+        self.asset: Articulation = env.scene[cfg.params["asset_cfg"].name]
+        # match foot body names with corresponding foot body ids
+        synced_feet_pair_names = cfg.params["synced_feet_pair_names"]
+        if (
+            len(synced_feet_pair_names) != 2
+            or len(synced_feet_pair_names[0]) != 2
+            or len(synced_feet_pair_names[1]) != 2
+        ):
+            raise ValueError("This reward only supports gaits with two pairs of synchronized feet, like trotting.")
+        synced_feet_pair_0 = self.contact_sensor.find_bodies(synced_feet_pair_names[0])[0]
+        synced_feet_pair_1 = self.contact_sensor.find_bodies(synced_feet_pair_names[1])[0]
+        self.synced_feet_pairs = [synced_feet_pair_0, synced_feet_pair_1]
+
+    def __call__(
+        self,
+        env: ManagerBasedRLEnv,
+        std: float,
+        command_name: str,
+        max_err: float,
+        velocity_threshold: float,
+        command_threshold: float,
+        synced_feet_pair_names,
+        asset_cfg: SceneEntityCfg,
+        sensor_cfg: SceneEntityCfg,
+    ) -> torch.Tensor:
+        """Compute the reward.
+
+        This reward is defined as a multiplication between six terms where two of them enforce pair feet
+        being in sync and the other four rewards if all the other remaining pairs are out of sync
+
+        Args:
+            env: The RL environment instance.
+        Returns:
+            The reward value.
+        """
+        # for synchronous feet, the contact (air) times of two feet should match
+        sync_reward_0 = self._sync_reward_func(self.synced_feet_pairs[0][0], self.synced_feet_pairs[0][1])
+        sync_reward_1 = self._sync_reward_func(self.synced_feet_pairs[1][0], self.synced_feet_pairs[1][1])
+        sync_reward = sync_reward_0 * sync_reward_1
+        # for asynchronous feet, the contact time of one foot should match the air time of the other one
+        async_reward_0 = self._async_reward_func(self.synced_feet_pairs[0][0], self.synced_feet_pairs[1][0])
+        async_reward_1 = self._async_reward_func(self.synced_feet_pairs[0][1], self.synced_feet_pairs[1][1])
+        async_reward_2 = self._async_reward_func(self.synced_feet_pairs[0][0], self.synced_feet_pairs[1][1])
+        async_reward_3 = self._async_reward_func(self.synced_feet_pairs[1][0], self.synced_feet_pairs[0][1])
+        async_reward = async_reward_0 * async_reward_1 * async_reward_2 * async_reward_3
+        # only enforce gait if cmd > 0
+        cmd = torch.linalg.norm(env.command_manager.get_command(self.command_name), dim=1)
+        body_vel = torch.linalg.norm(self.asset.data.root_com_lin_vel_b[:, :2], dim=1)
+        reward = torch.where(
+            torch.logical_or(cmd > self.command_threshold, body_vel > self.velocity_threshold),
+            sync_reward * async_reward,
+            0.0,
+        )
+        # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+        return reward
+
+    """
+    Helper functions.
+    """
+
+    def _sync_reward_func(self, foot_0: int, foot_1: int) -> torch.Tensor:
+        """Reward synchronization of two feet."""
+        air_time = self.contact_sensor.data.current_air_time
+        contact_time = self.contact_sensor.data.current_contact_time
+        # penalize the difference between the most recent air time and contact time of synced feet pairs.
+        se_air = torch.clip(torch.square(air_time[:, foot_0] - air_time[:, foot_1]), max=self.max_err**2)
+        se_contact = torch.clip(torch.square(contact_time[:, foot_0] - contact_time[:, foot_1]), max=self.max_err**2)
+        return torch.exp(-(se_air + se_contact) / self.std)
+
+    def _async_reward_func(self, foot_0: int, foot_1: int) -> torch.Tensor:
+        """Reward anti-synchronization of two feet."""
+        air_time = self.contact_sensor.data.current_air_time
+        contact_time = self.contact_sensor.data.current_contact_time
+        # penalize the difference between opposing contact modes air time of feet 1 to contact time of feet 2
+        # and contact time of feet 1 to air time of feet 2) of feet pairs that are not in sync with each other.
+        se_act_0 = torch.clip(torch.square(air_time[:, foot_0] - contact_time[:, foot_1]), max=self.max_err**2)
+        se_act_1 = torch.clip(torch.square(contact_time[:, foot_0] - air_time[:, foot_1]), max=self.max_err**2)
+        return torch.exp(-(se_act_0 + se_act_1) / self.std)
+
+
+def joint_mirror(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, mirror_joints: list[list[str]]) -> torch.Tensor:
+    # extract the used quantities (to enable type-hinting)
+    asset: Articulation = env.scene[asset_cfg.name]
+    if not hasattr(env, "joint_mirror_joints_cache") or env.joint_mirror_joints_cache is None:
+        # Cache joint positions for all pairs
+        env.joint_mirror_joints_cache = [
+            [asset.find_joints(joint_name) for joint_name in joint_pair] for joint_pair in mirror_joints
+        ]
+    reward = torch.zeros(env.num_envs, device=env.device)
+    # Iterate over all joint pairs
+    for joint_pair in env.joint_mirror_joints_cache:
+        # Calculate the difference for each pair and add to the total reward
+        diff = torch.sum(
+            torch.square(asset.data.joint_pos[:, joint_pair[0][0]] - asset.data.joint_pos[:, joint_pair[1][0]]),
+            dim=-1,
+        )
+        reward += diff
+    reward *= 1 / len(mirror_joints) if len(mirror_joints) > 0 else 0
+    reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def action_mirror(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, mirror_joints: list[list[str]]) -> torch.Tensor:
+    # extract the used quantities (to enable type-hinting)
+    asset: Articulation = env.scene[asset_cfg.name]
+    if not hasattr(env, "action_mirror_joints_cache") or env.action_mirror_joints_cache is None:
+        # Cache joint positions for all pairs
+        env.action_mirror_joints_cache = [
+            [asset.find_joints(joint_name) for joint_name in joint_pair] for joint_pair in mirror_joints
+        ]
+    reward = torch.zeros(env.num_envs, device=env.device)
+    # Iterate over all joint pairs
+    for joint_pair in env.action_mirror_joints_cache:
+        # Calculate the difference for each pair and add to the total reward
+        diff = torch.sum(
+            torch.square(
+                torch.abs(env.action_manager.action[:, joint_pair[0][0]])
+                - torch.abs(env.action_manager.action[:, joint_pair[1][0]])
+            ),
+            dim=-1,
+        )
+        reward += diff
+    reward *= 1 / len(mirror_joints) if len(mirror_joints) > 0 else 0
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def action_sync(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg, joint_groups: list[list[str]]) -> torch.Tensor:
+    # extract the used quantities (to enable type-hinting)
+    asset: Articulation = env.scene[asset_cfg.name]
+
+    # Cache joint indices if not already done
+    if not hasattr(env, "action_sync_joint_cache") or env.action_sync_joint_cache is None:
+        env.action_sync_joint_cache = [
+            [asset.find_joints(joint_name) for joint_name in joint_group] for joint_group in joint_groups
+        ]
+
+    reward = torch.zeros(env.num_envs, device=env.device)
+    # Iterate over each joint group
+    for joint_group in env.action_sync_joint_cache:
+        if len(joint_group) < 2:
+            continue  # need at least 2 joints to compare
+
+        # Get absolute actions for all joints in this group
+        actions = torch.stack(
+            [torch.abs(env.action_manager.action[:, joint[0]]) for joint in joint_group], dim=1
+        )  # shape: (num_envs, num_joints_in_group)
+
+        # Calculate mean action for each environment
+        mean_actions = torch.mean(actions, dim=1, keepdim=True)
+
+        # Calculate variance from mean for each joint
+        variance = torch.mean(torch.square(actions - mean_actions), dim=1)
+
+        # Add to reward (we want to minimize this variance)
+        reward += variance.squeeze()
+    reward *= 1 / len(joint_groups) if len(joint_groups) > 0 else 0
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+# def feet_air_time(
+#     env: ManagerBasedRLEnv, command_name: str, sensor_cfg: SceneEntityCfg, threshold: float
+# ) -> torch.Tensor:
+#     """Reward long steps taken by the feet using L2-kernel.
+
+#     This function rewards the agent for taking steps that are longer than a threshold. This helps ensure
+#     that the robot lifts its feet off the ground and takes steps. The reward is computed as the sum of
+#     the time for which the feet are in the air.
+
+#     If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero.
+#     """
+#     # extract the used quantities (to enable type-hinting)
+#     contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+#     # compute the reward
+#     first_contact = contact_sensor.compute_first_contact(env.step_dt)[:, sensor_cfg.body_ids]
+#     last_air_time = contact_sensor.data.last_air_time[:, sensor_cfg.body_ids]
+#     reward = torch.sum((last_air_time - threshold) * first_contact, dim=1)
+#     # no reward for zero command
+#     reward *= torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1) > 0.1
+#     # print(torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1), "command norm")
+#     reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    # return reward
+
+# def feet_air_time(
+#     env: ManagerBasedRLEnv,
+#     asset_cfg: SceneEntityCfg,
+#     sensor_cfg: SceneEntityCfg,
+#     mode_time: float,
+#     velocity_threshold: float,
+# ) -> torch.Tensor:
+#     """Reward longer feet air and contact time."""
+#     # extract the used quantities (to enable type-hinting)
+#     contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+#     asset: Articulation = env.scene[asset_cfg.name]
+#     if contact_sensor.cfg.track_air_time is False:
+#         raise RuntimeError("Activate ContactSensor's track_air_time!")
+#     # compute the reward
+#     current_air_time = contact_sensor.data.current_air_time[:, sensor_cfg.body_ids]
+#     current_contact_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids]
+
+#     t_max = torch.max(current_air_time, current_contact_time)
+#     t_min = torch.clip(t_max, max=mode_time)
+#     stance_cmd_reward = torch.clip(current_contact_time - current_air_time, -mode_time, mode_time)
+#     cmd = torch.norm(env.command_manager.get_command("base_velocity"), dim=1).unsqueeze(dim=1).expand(-1, 4)
+#     body_vel = torch.linalg.norm(asset.data.root_lin_vel_b[:, :2], dim=1).unsqueeze(dim=1).expand(-1, 4)
+#     reward = torch.where(
+#         torch.logical_or(cmd > 0.0, body_vel > velocity_threshold),
+#         torch.where(t_max < mode_time, t_min, 0),
+#         stance_cmd_reward,
+#     )
+#     return torch.sum(reward, dim=1)
+
+
+def feet_air_time_positive_biped(env, command_name: str, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor:
+    """Reward long steps taken by the feet for bipeds.
+
+    This function rewards the agent for taking steps up to a specified threshold and also keep one foot at
+    a time in the air.
+
+    If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero.
+    """
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    # compute the reward
+    air_time = contact_sensor.data.current_air_time[:, sensor_cfg.body_ids]
+    contact_time = contact_sensor.data.current_contact_time[:, sensor_cfg.body_ids]
+    in_contact = contact_time > 0.0
+    in_mode_time = torch.where(in_contact, contact_time, air_time)
+    single_stance = torch.sum(in_contact.int(), dim=1) == 1
+    reward = torch.min(torch.where(single_stance.unsqueeze(-1), in_mode_time, 0.0), dim=1)[0]
+    reward = torch.clamp(reward, max=threshold)
+    # no reward for zero command
+    reward *= torch.norm(env.command_manager.get_command(command_name)[:, :2], dim=1) > 0.1
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_air_time_variance_penalty(env: ManagerBasedRLEnv, sensor_cfg: SceneEntityCfg) -> torch.Tensor:
+    """Penalize variance in the amount of time each foot spends in the air/on the ground relative to each other"""
+    # extract the used quantities (to enable type-hinting)
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    # compute the reward
+    last_air_time = contact_sensor.data.last_air_time[:, sensor_cfg.body_ids]
+    last_contact_time = contact_sensor.data.last_contact_time[:, sensor_cfg.body_ids]
+    reward = torch.var(torch.clip(last_air_time, max=0.5), dim=1) + torch.var(
+        torch.clip(last_contact_time, max=0.5), dim=1)
+    # print(last_air_time, "last air time")
+    # print(last_contact_time, "last contact time")
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+
+
+def feet_contact(
+    env: ManagerBasedRLEnv, command_name: str, expect_contact_num: int, sensor_cfg: SceneEntityCfg
+) -> torch.Tensor:
+    """Reward feet contact"""
+    # extract the used quantities (to enable type-hinting)
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    # compute the reward
+    contact = contact_sensor.compute_first_contact(env.step_dt)[:, sensor_cfg.body_ids]
+    contact_num = torch.sum(contact, dim=1)
+    reward = (contact_num != expect_contact_num).float()
+    # no reward for zero command
+    reward *= torch.linalg.norm(env.command_manager.get_command(command_name), dim=1) > 0.5
+    reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_contact_without_cmd(env: ManagerBasedRLEnv, command_name: str, sensor_cfg: SceneEntityCfg) -> torch.Tensor:
+    """Reward feet contact"""
+    # extract the used quantities (to enable type-hinting)
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    # compute the reward
+    contact = contact_sensor.compute_first_contact(env.step_dt)[:, sensor_cfg.body_ids]
+    # print(contact, "contact")
+    reward = torch.sum(contact, dim=-1).float()
+    # print(reward, "reward after sum")
+    reward *= torch.linalg.norm(env.command_manager.get_command(command_name), dim=1) < 0.5
+    # print(env.command_manager.get_command(command_name), "env.command_manager.get_command(command_name)")
+    # print(reward, "reward after multiply")
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_stumble(env: ManagerBasedRLEnv, sensor_cfg: SceneEntityCfg) -> torch.Tensor:
+    # extract the used quantities (to enable type-hinting)
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    forces_z = torch.abs(contact_sensor.data.net_forces_w[:, sensor_cfg.body_ids, 2])
+    forces_xy = torch.linalg.norm(contact_sensor.data.net_forces_w[:, sensor_cfg.body_ids, :2], dim=2)
+    # Penalize feet hitting vertical surfaces
+    reward = torch.any(forces_xy > 4 * forces_z, dim=1).float()
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_distance_y_exp(
+    env: ManagerBasedRLEnv, stance_width: float, std: float, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+) -> torch.Tensor:
+    asset: RigidObject = env.scene[asset_cfg.name]
+    cur_footsteps_translated = asset.data.body_link_pos_w[:, asset_cfg.body_ids, :] - asset.data.root_link_pos_w[
+        :, :
+    ].unsqueeze(1)
+    n_feet = len(asset_cfg.body_ids)
+    footsteps_in_body_frame = torch.zeros(env.num_envs, n_feet, 3, device=env.device)
+    for i in range(n_feet):
+        footsteps_in_body_frame[:, i, :] = math_utils.quat_apply(
+            math_utils.quat_conjugate(asset.data.root_link_quat_w), cur_footsteps_translated[:, i, :]
+        )
+    side_sign = torch.tensor(
+        [1.0 if i % 2 == 0 else -1.0 for i in range(n_feet)],
+        device=env.device,
+    )
+    stance_width_tensor = stance_width * torch.ones([env.num_envs, 1], device=env.device)
+    desired_ys = stance_width_tensor / 2 * side_sign.unsqueeze(0)
+    stance_diff = torch.square(desired_ys - footsteps_in_body_frame[:, :, 1])
+    reward = torch.exp(-torch.sum(stance_diff, dim=1) / (std**2))
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_distance_xy_exp(
+    env: ManagerBasedRLEnv,
+    stance_width: float,
+    stance_length: float,
+    std: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+) -> torch.Tensor:
+    asset: RigidObject = env.scene[asset_cfg.name]
+
+    # Compute the current footstep positions relative to the root
+    cur_footsteps_translated = asset.data.body_link_pos_w[:, asset_cfg.body_ids, :] - asset.data.root_link_pos_w[
+        :, :
+    ].unsqueeze(1)
+
+    footsteps_in_body_frame = torch.zeros(env.num_envs, 4, 3, device=env.device)
+    for i in range(4):
+        footsteps_in_body_frame[:, i, :] = math_utils.quat_apply(
+            math_utils.quat_conjugate(asset.data.root_link_quat_w), cur_footsteps_translated[:, i, :]
+        )
+
+    # Desired x and y positions for each foot
+    stance_width_tensor = stance_width * torch.ones([env.num_envs, 1], device=env.device)
+    stance_length_tensor = stance_length * torch.ones([env.num_envs, 1], device=env.device)
+
+    desired_xs = torch.cat(
+        [stance_length_tensor / 2, stance_length_tensor / 2, -stance_length_tensor / 2, -stance_length_tensor / 2],
+        dim=1,
+    )
+    desired_ys = torch.cat(
+        [stance_width_tensor / 2, -stance_width_tensor / 2, stance_width_tensor / 2, -stance_width_tensor / 2], dim=1
+    )
+
+    # Compute differences in x and y
+    stance_diff_x = torch.square(desired_xs - footsteps_in_body_frame[:, :, 0])
+    stance_diff_y = torch.square(desired_ys - footsteps_in_body_frame[:, :, 1])
+
+    # Combine x and y differences and compute the exponential penalty
+    stance_diff = stance_diff_x + stance_diff_y
+    reward = torch.exp(-torch.sum(stance_diff, dim=1) / std**2)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_height(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    asset_cfg: SceneEntityCfg,
+    target_height: float,
+    tanh_mult: float,
+) -> torch.Tensor:
+    """Reward the swinging feet for clearing a specified height off the ground"""
+    asset: RigidObject = env.scene[asset_cfg.name]
+    foot_z_target_error = torch.square(asset.data.body_pos_w[:, asset_cfg.body_ids, 2] - target_height)
+    # foot_velocity_tanh = torch.tanh(
+    #     tanh_mult * torch.linalg.norm(asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :2], dim=2)
+    # )
+    # reward = torch.sum(foot_z_target_error * foot_velocity_tanh, dim=1)
+    reward = torch.sum(foot_z_target_error, dim=1)
+    # print(foot_z_target_error, "foot_z_target_error")
+    # no reward for zero command
+    reward *= torch.linalg.norm(env.command_manager.get_command(command_name), dim=1) > 0.2
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_height_body(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    asset_cfg: SceneEntityCfg,
+    target_height: float,
+    tanh_mult: float,
+) -> torch.Tensor:
+    """Reward the swinging feet for clearing a specified height off the ground"""
+    asset: RigidObject = env.scene[asset_cfg.name]
+    cur_footpos_translated = asset.data.body_pos_w[:, asset_cfg.body_ids, :] - asset.data.root_pos_w[:, :].unsqueeze(1)
+    footpos_in_body_frame = torch.zeros(env.num_envs, len(asset_cfg.body_ids), 3, device=env.device)
+    cur_footvel_translated = asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :] - asset.data.root_lin_vel_w[
+        :, :
+    ].unsqueeze(1)
+    footvel_in_body_frame = torch.zeros(env.num_envs, len(asset_cfg.body_ids), 3, device=env.device)
+    for i in range(len(asset_cfg.body_ids)):
+        footpos_in_body_frame[:, i, :] = math_utils.quat_apply_inverse(
+            asset.data.root_quat_w, cur_footpos_translated[:, i, :]
+        )
+        footvel_in_body_frame[:, i, :] = math_utils.quat_apply_inverse(
+            asset.data.root_quat_w, cur_footvel_translated[:, i, :]
+        )
+    foot_z_target_error = torch.square(footpos_in_body_frame[:, :, 2] - target_height).view(env.num_envs, -1)
+    foot_velocity_tanh = torch.tanh(tanh_mult * torch.norm(footvel_in_body_frame[:, :, :2], dim=2))
+    reward = torch.sum(foot_z_target_error * foot_velocity_tanh, dim=1)
+    reward *= torch.linalg.norm(env.command_manager.get_command(command_name), dim=1) > 0.1
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def feet_slide(
+    env: ManagerBasedRLEnv, sensor_cfg: SceneEntityCfg, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+) -> torch.Tensor:
+    """Penalize feet sliding.
+
+    This function penalizes the agent for sliding its feet on the ground. The reward is computed as the
+    norm of the linear velocity of the feet multiplied by a binary contact sensor. This ensures that the
+    agent is penalized only when the feet are in contact with the ground.
+    """
+    # Penalize feet sliding
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    contacts = contact_sensor.data.net_forces_w_history[:, :, sensor_cfg.body_ids, :].norm(dim=-1).max(dim=1)[0] > 1.0
+    asset: RigidObject = env.scene[asset_cfg.name]
+
+    # feet_vel = asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :2]
+    # reward = torch.sum(feet_vel.norm(dim=-1) * contacts, dim=1)
+
+    cur_footvel_translated = asset.data.body_lin_vel_w[:, asset_cfg.body_ids, :] - asset.data.root_lin_vel_w[
+        :, :
+    ].unsqueeze(1)
+    footvel_in_body_frame = torch.zeros(env.num_envs, len(asset_cfg.body_ids), 3, device=env.device)
+    for i in range(len(asset_cfg.body_ids)):
+        footvel_in_body_frame[:, i, :] = math_utils.quat_apply_inverse(
+            asset.data.root_quat_w, cur_footvel_translated[:, i, :]
+        )
+    foot_leteral_vel = torch.sqrt(torch.sum(torch.square(footvel_in_body_frame[:, :, :2]), dim=2)).view(
+        env.num_envs, -1
+    )
+    reward = torch.sum(foot_leteral_vel * contacts, dim=1)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+# def stand_still_joint_deviation_l1(
+#     env, command_name: str, command_threshold: float = 0.06, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")
+# ) -> torch.Tensor:
+#     """Penalize offsets from the default joint positions when the command is very small."""
+    # command = env.command_manager.get_command(command_name)
+#     # Penalize motion when command is nearly zero.
+#     return joint_deviation_l1(env, asset_cfg) * (torch.norm(command[:, :], dim=1) < command_threshold)
+
+# def joint_deviation_l1(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+#     """Penalize joint positions that deviate from the default one."""
+#     # extract the used quantities (to enable type-hinting)
+#     asset: Articulation = env.scene[asset_cfg.name]
+#     # compute out of limits constraints
+#     angle = asset.data.joint_pos[:, asset_cfg.joint_ids] - asset.data.default_joint_pos[:, asset_cfg.joint_ids]
+#     return torch.sum(torch.abs(angle), dim=1)
+
+
+# def smoothness_1(env: ManagerBasedRLEnv) -> torch.Tensor:
+#     # Penalize changes in actions
+#     diff = torch.square(env.action_manager.action - env.action_manager.prev_action)
+#     diff = diff * (env.action_manager.prev_action[:, :] != 0)  # ignore first step
+#     return torch.sum(diff, dim=1)
+
+
+# def joint_acc_l2_new(env: ManagerBasedRLEnv) -> torch.Tensor:
+
+# def smoothness_2(env: ManagerBasedRLEnv) -> torch.Tensor:
+#     # Penalize changes in actions
+#     diff = torch.square(env.action_manager.action - 2 * env.action_manager.prev_action + env.action_manager.prev_prev_action)
+#     diff = diff * (env.action_manager.prev_action[:, :] != 0)  # ignore first step
+#     diff = diff * (env.action_manager.prev_prev_action[:, :] != 0)  # ignore second step
+#     # print(torch.sum(diff, dim=1), "smoothness l2")
+#     return torch.sum(diff, dim=1)
+
+
+def upward(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Penalize z-axis base linear velocity using L2 squared kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    reward = torch.square(1 - asset.data.projected_gravity_b[:, 2])
+    return reward
+
+
+
+
+
+def base_height_l2(
+    env: ManagerBasedRLEnv,
+    target_height: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+    sensor_cfg: SceneEntityCfg | None = None,
+) -> torch.Tensor:
+    """Penalize asset height from its target using L2 squared kernel.
+
+    Note:
+        For flat terrain, target height is in the world frame. For rough terrain,
+        sensor readings can adjust the target height to account for the terrain.
+    """
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    if sensor_cfg is not None:
+        sensor: RayCaster = env.scene[sensor_cfg.name]
+        # Adjust the target height using the sensor data
+        ray_hits = sensor.data.ray_hits_w[..., 2]
+        if torch.isnan(ray_hits).any() or torch.isinf(ray_hits).any() or torch.max(torch.abs(ray_hits)) > 1e6:
+            adjusted_target_height = asset.data.root_link_pos_w[:, 2]
+        else:
+            adjusted_target_height = target_height + torch.mean(ray_hits, dim=1)
+    else:
+        # Use the provided target height directly for flat terrain
+        adjusted_target_height = target_height
+    # Compute the L2 squared penalty
+    reward = torch.square(asset.data.root_pos_w[:, 2] - adjusted_target_height)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def lin_vel_z_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Penalize z-axis base linear velocity using L2 squared kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    reward = torch.square(asset.data.root_lin_vel_b[:, 2])
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def ang_vel_xy_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Penalize xy-axis base angular velocity using L2 squared kernel."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    reward = torch.sum(torch.square(asset.data.root_ang_vel_b[:, :2]), dim=1)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def undesired_contacts(env: ManagerBasedRLEnv, threshold: float, sensor_cfg: SceneEntityCfg) -> torch.Tensor:
+    """Penalize undesired contacts as the number of violations that are above a threshold."""
+    # extract the used quantities (to enable type-hinting)
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    # check if contact force is above threshold
+    net_contact_forces = contact_sensor.data.net_forces_w_history
+    is_contact = torch.max(torch.norm(net_contact_forces[:, :, sensor_cfg.body_ids], dim=-1), dim=1)[0] > threshold
+    # sum over contacts for each environment
+    reward = torch.sum(is_contact, dim=1).float()
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def flat_orientation_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Penalize non-flat base orientation using L2 squared kernel.
+
+    This is computed by penalizing the xy-components of the projected gravity vector.
+    """
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    reward = torch.sum(torch.square(asset.data.projected_gravity_b[:, :2]), dim=1)
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
+
+
+def roll_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Penalize body roll angle in radians (squared)."""
+    asset: RigidObject = env.scene[asset_cfg.name]
+    roll, _, _ = math_utils.euler_xyz_from_quat(asset.data.root_quat_w)
+    return torch.square(roll)
+
+
+def pitch_l2(env: ManagerBasedRLEnv, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")) -> torch.Tensor:
+    """Penalize body pitch angle in radians (squared)."""
+    asset: RigidObject = env.scene[asset_cfg.name]
+    _, pitch, _ = math_utils.euler_xyz_from_quat(asset.data.root_quat_w)
+    return torch.square(pitch)
+
+
+def feet_air_time_including_ang_z(
+    env: ManagerBasedRLEnv, command_name: str, sensor_cfg: SceneEntityCfg, threshold: float
+) -> torch.Tensor:
+    """Reward long steps taken by the feet using L2-kernel.
+
+    This function rewards the agent for taking steps that are longer than a threshold. This helps ensure
+    that the robot lifts its feet off the ground and takes steps. The reward is computed as the sum of
+    the time for which the feet are in the air.
+
+    If the commands are small (i.e. the agent is not supposed to take a step), then the reward is zero.
+    """
+    # extract the used quantities (to enable type-hinting)
+    contact_sensor: ContactSensor = env.scene.sensors[sensor_cfg.name]
+    # compute the reward
+    first_contact = contact_sensor.compute_first_contact(env.step_dt)[:, sensor_cfg.body_ids]
+    last_air_time = contact_sensor.data.last_air_time[:, sensor_cfg.body_ids]
+    reward = torch.sum((last_air_time - threshold) * first_contact, dim=1)
+    # no reward for zero command
+    reward *= torch.norm(env.command_manager.get_command(command_name), dim=1) > 0.1
+    # reward *= torch.norm(env.command_manager.get_command(command_name)[:, :3], dim=1) > 0.1
+    return reward
+
+def lin_vel_xy_l2_with_ang_z_command(
+    env: ManagerBasedRLEnv,
+    command_name: str,
+    command_threshold: float,
+    asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
+    ) -> torch.Tensor:
+    """Penalize xy-axis base linear velocity using L2 squared kernel if command is ang_vel_z."""
+    # extract the used quantities (to enable type-hinting)
+    asset: RigidObject = env.scene[asset_cfg.name]
+    # reward = torch.square(asset.data.root_lin_vel_b[:, 2])
+    reward = torch.sum(torch.square(asset.data.root_lin_vel_b[:, :2]), dim=1)
+    command = env.command_manager.get_command(command_name)
+    reward *= (torch.sum(torch.square(command[:, 2:]), dim=1) > command_threshold) & \
+            (torch.sum(torch.square(command[:, :2]), dim=1) < command_threshold)
+    # reward *= torch.sum(torch.square(env.command_manager.get_command(command_name)[:, 2:]), dim=1) > command_threshold
+    # reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
+    return reward
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/terrain.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/terrain.py
new file mode 100644
index 0000000..755ce1d
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/mdp/terrain.py
@@ -0,0 +1,107 @@
+"""Custom terrains for the FireAnt locomotion tasks."""
+
+from __future__ import annotations
+
+import numpy as np
+import trimesh
+
+from isaaclab.terrains import HfTerrainBaseCfg, SubTerrainBaseCfg
+from isaaclab.terrains.height_field.utils import height_field_to_mesh
+from isaaclab.utils import configclass
+
+
+def single_side_bridge_terrain(
+    difficulty: float, cfg: "SingleSideBridgeTerrainCfg"
+) -> tuple[list[trimesh.Trimesh], np.ndarray]:
+    """Long raised bridge on the +y half of the cell; flat ground on the -y half.
+
+    The bridge edge is exactly at the cell center line (local ``y = size[1] / 2``),
+    so a robot spawned at the cell center with zero yaw has its left wheels on the
+    bridge and its right wheels on the flat ground.
+    """
+    height = cfg.bridge_height_range[0] + difficulty * (
+        cfg.bridge_height_range[1] - cfg.bridge_height_range[0]
+    )
+
+    # Ground slab: top surface at z=0, extends over the whole cell.
+    ground = trimesh.creation.box(
+        extents=(cfg.size[0], cfg.size[1], 0.05),
+        transform=trimesh.transformations.translation_matrix(
+            (cfg.size[0] / 2.0, cfg.size[1] / 2.0, -0.025)
+        ),
+    )
+
+    # Bridge: full cell length along x, raised strip on the +y side.
+    bridge = trimesh.creation.box(
+        extents=(cfg.size[0], cfg.bridge_width, height),
+        transform=trimesh.transformations.translation_matrix(
+            (
+                cfg.size[0] / 2.0,
+                cfg.size[1] / 2.0 + cfg.bridge_width / 2.0,
+                height / 2.0,
+            )
+        ),
+    )
+
+    origin = np.array((cfg.size[0] / 2.0, cfg.size[1] / 2.0, 0.0))
+    return [ground, bridge], origin
+
+
+@configclass
+class SingleSideBridgeTerrainCfg(SubTerrainBaseCfg):
+    """Configuration for a long single-side bridge terrain."""
+
+    function = single_side_bridge_terrain
+
+    bridge_height_range: tuple[float, float] = (0.06, 0.14)
+    """Bridge height at the start and end of the difficulty curriculum (m)."""
+
+    bridge_width: float = 1.5
+    """Width of the raised bridge on the +y side of the cell (m)."""
+
+
+@height_field_to_mesh
+def smooth_random_terrain(difficulty: float, cfg: "SmoothRandomTerrainCfg") -> np.ndarray:
+    """Smooth low-frequency random height field.
+
+    The surface is a sum of a few low-frequency sinusoids. It has no steps or
+    sharp edges, so the robot has to keep its body level over gentle, random
+    pitch and roll disturbances.
+    """
+    width_pixels = int(cfg.size[0] / cfg.horizontal_scale)
+    length_pixels = int(cfg.size[1] / cfg.horizontal_scale)
+    x = np.arange(width_pixels, dtype=np.float64) * cfg.horizontal_scale
+    y = np.arange(length_pixels, dtype=np.float64) * cfg.horizontal_scale
+    grid_x, grid_y = np.meshgrid(x, y, indexing="ij")
+
+    amplitude = cfg.height_range[0] + difficulty * (cfg.height_range[1] - cfg.height_range[0])
+    heights = np.zeros_like(grid_x)
+    for _ in range(cfg.num_waves):
+        direction = np.random.uniform(0.0, 2.0 * np.pi)
+        wavelength = np.random.uniform(*cfg.wavelength_range)
+        phase = np.random.uniform(0.0, 2.0 * np.pi)
+        weight = np.random.uniform(0.5, 1.0)
+        projection = grid_x * np.cos(direction) + grid_y * np.sin(direction)
+        heights += weight * np.sin(2.0 * np.pi * projection / wavelength + phase)
+
+    heights -= heights.mean()
+    peak = float(np.max(np.abs(heights)))
+    if peak > 1e-9:
+        heights *= amplitude / peak
+    return np.rint(heights / cfg.vertical_scale).astype(np.int16)
+
+
+@configclass
+class SmoothRandomTerrainCfg(HfTerrainBaseCfg):
+    """Configuration for a smooth random-undulation terrain."""
+
+    function = smooth_random_terrain
+
+    height_range: tuple[float, float] = (0.02, 0.05)
+    """Peak height of the undulations at the start and end of the curriculum (m)."""
+
+    wavelength_range: tuple[float, float] = (1.5, 3.0)
+    """Spatial wavelength range of the undulations (m)."""
+
+    num_waves: int = 3
+    """Number of sinusoids summed to form the surface."""
diff --git a/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/velocity_env_cfg.py b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/velocity_env_cfg.py
new file mode 100644
index 0000000..46a9866
--- /dev/null
+++ b/source/rl_training/rl_training/tasks/manager_based/locomotion/velocity/velocity_env_cfg.py
@@ -0,0 +1,868 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+
+# Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import inspect
+import math
+import sys
+from dataclasses import MISSING
+
+import isaaclab.sim as sim_utils
+from isaaclab.assets import ArticulationCfg, AssetBaseCfg
+from isaaclab.envs import ManagerBasedRLEnvCfg
+from isaaclab.managers import CurriculumTermCfg as CurrTerm
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import ObservationGroupCfg as ObsGroup
+from isaaclab.managers import ObservationTermCfg as ObsTerm
+from isaaclab.managers import RewardTermCfg as RewTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.managers import TerminationTermCfg as DoneTerm
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.sensors import ContactSensorCfg, RayCasterCfg, patterns
+from isaaclab.terrains import TerrainImporterCfg
+from isaaclab.utils import configclass
+from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise
+
+import rl_training.tasks.manager_based.locomotion.velocity.mdp as mdp
+
+##
+# Pre-defined configs
+##
+from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG  # isort: skip
+
+
+##
+# Scene definition
+##
+
+
+@configclass
+class MySceneCfg(InteractiveSceneCfg):
+    """Configuration for the terrain scene with a legged robot."""
+
+    # ground terrain
+    terrain = TerrainImporterCfg(
+        prim_path="/World/ground",
+        terrain_type="generator",
+        terrain_generator=ROUGH_TERRAINS_CFG,
+        max_init_terrain_level=5,
+        collision_group=-1,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=1.0,
+        ),
+        # Keep scene startup independent of NVIDIA's online asset server.
+        # The robot visual geometry is still loaded from the STL meshes in its URDF.
+        visual_material=sim_utils.PreviewSurfaceCfg(
+            diffuse_color=(0.18, 0.20, 0.22),
+            roughness=0.9,
+        ),
+        debug_vis=False,
+    )
+    # robots
+    robot: ArticulationCfg = MISSING
+    # sensors
+    height_scanner = RayCasterCfg(
+        prim_path="{ENV_REGEX_NS}/Robot/base",
+        offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
+        ray_alignment="yaw",
+        pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
+        debug_vis=False,
+        mesh_prim_paths=["/World/ground"],
+    )
+    height_scanner_base = RayCasterCfg(
+        prim_path="{ENV_REGEX_NS}/Robot/base",
+        offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
+        ray_alignment="yaw",
+        pattern_cfg=patterns.GridPatternCfg(resolution=0.05, size=(0.1, 0.1)),
+        debug_vis=False,
+        mesh_prim_paths=["/World/ground"],
+    )
+    contact_forces = ContactSensorCfg(prim_path="{ENV_REGEX_NS}/Robot/.*", history_length=3, track_air_time=True)
+    # lights
+    sky_light = AssetBaseCfg(
+        prim_path="/World/skyLight",
+        spawn=sim_utils.DomeLightCfg(
+            intensity=1200.0,
+            color=(0.75, 0.75, 0.75),
+        ),
+    )
+
+
+##
+# MDP settings
+##
+
+
+@configclass
+class CommandsCfg:
+    """Command specifications for the MDP."""
+
+    base_velocity = mdp.UniformThresholdVelocityCommandCfg(
+        asset_name="robot",
+        resampling_time_range=(10.0, 10.0),
+        rel_standing_envs=0.02,
+        rel_heading_envs=1.0,
+        heading_command=True,
+        heading_control_stiffness=0.5,
+        debug_vis=True,
+        ranges=mdp.UniformThresholdVelocityCommandCfg.Ranges(
+            lin_vel_x=(-1.0, 1.0), lin_vel_y=(-1.0, 1.0), ang_vel_z=(-1.0, 1.0), heading=(-math.pi, math.pi)
+        ),
+    )
+
+
+@configclass
+class ActionsCfg:
+    """Action specifications for the MDP."""
+
+    joint_pos = mdp.JointPositionActionCfg(
+        asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True, clip=None, preserve_order=True
+    )
+
+
+@configclass
+class ObservationsCfg:
+    """Observation specifications for the MDP."""
+
+    @configclass
+    class PolicyCfg(ObsGroup):
+        """Observations for policy group."""
+
+        # observation terms (order preserved)
+        base_lin_vel = ObsTerm(
+            func=mdp.base_lin_vel,
+            noise=Unoise(n_min=-0.1, n_max=0.1),
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        base_ang_vel = ObsTerm(
+            func=mdp.base_ang_vel,
+            noise=Unoise(n_min=-0.2, n_max=0.2),
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        projected_gravity = ObsTerm(
+            func=mdp.projected_gravity,
+            noise=Unoise(n_min=-0.05, n_max=0.05),
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        velocity_commands = ObsTerm(
+            func=mdp.generated_commands,
+            params={"command_name": "base_velocity"},
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        joint_pos = ObsTerm(
+            func=mdp.joint_pos_rel,
+            params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*", preserve_order=True)},
+            noise=Unoise(n_min=-0.01, n_max=0.01),
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        joint_vel = ObsTerm(
+            func=mdp.joint_vel_rel,
+            params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*", preserve_order=True)},
+            noise=Unoise(n_min=-1.5, n_max=1.5),
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        actions = ObsTerm(
+            func=mdp.last_action,
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        height_scan = ObsTerm(
+            func=mdp.height_scan,
+            params={"sensor_cfg": SceneEntityCfg("height_scanner")},
+            noise=Unoise(n_min=-0.1, n_max=0.1),
+            clip=(-1.0, 1.0),
+            scale=1.0,
+        )
+
+        def __post_init__(self):
+            self.enable_corruption = True
+            self.concatenate_terms = True
+
+    @configclass
+    class CriticCfg(ObsGroup):
+        """Observations for critic group."""
+
+        # observation terms (order preserved)
+        base_lin_vel = ObsTerm(
+            func=mdp.base_lin_vel,
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        base_ang_vel = ObsTerm(
+            func=mdp.base_ang_vel,
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        projected_gravity = ObsTerm(
+            func=mdp.projected_gravity,
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        velocity_commands = ObsTerm(
+            func=mdp.generated_commands,
+            params={"command_name": "base_velocity"},
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        joint_pos = ObsTerm(
+            func=mdp.joint_pos_rel,
+            params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*", preserve_order=True)},
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        joint_vel = ObsTerm(
+            func=mdp.joint_vel_rel,
+            params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*", preserve_order=True)},
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        actions = ObsTerm(
+            func=mdp.last_action,
+            clip=(-100.0, 100.0),
+            scale=1.0,
+        )
+        height_scan = ObsTerm(
+            func=mdp.height_scan,
+            params={"sensor_cfg": SceneEntityCfg("height_scanner")},
+            clip=(-1.0, 1.0),
+            scale=1.0,
+        )
+        # joint_effort = ObsTerm(
+        #     func=mdp.joint_effort,
+        #     clip=(-100, 100),
+        #     scale=0.01,
+        # )
+
+        def __post_init__(self):
+            self.enable_corruption = False
+            self.concatenate_terms = True
+
+    # observation groups
+    policy: PolicyCfg = PolicyCfg()
+    critic: CriticCfg = CriticCfg()
+
+
+@configclass
+class EventCfg:
+    """Configuration for events."""
+
+    # startup
+    randomize_rigid_body_material = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=".*"),
+            "static_friction_range": (0.3, 1.0),
+            "dynamic_friction_range": (0.3, 0.8),
+            "restitution_range": (0.0, 0.4),
+            "num_buckets": 1024,
+        },
+    )
+
+    randomize_rigid_body_mass = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "mass_distribution_params": (0.85, 1.15),
+            "operation": "scale",
+            "recompute_inertia": True,
+        },
+    )
+
+    randomize_rigid_body_mass_base = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "mass_distribution_params": (-1.0, 3.0),
+            "operation": "add",
+            "recompute_inertia": True,
+        },
+    )
+
+    randomize_rigid_body_inertia = EventTerm(
+        func=mdp.randomize_rigid_body_inertia,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=".*"),
+            "inertia_distribution_params": (0.85, 1.15),
+            "operation": "scale",
+        },
+    )
+
+    randomize_com_positions = EventTerm(
+        func=mdp.randomize_rigid_body_com,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=".*"),
+            "com_range": {"x": (-0.03, 0.03), "y": (-0.03, 0.03), "z": (-0.02, 0.02)},
+        },
+    )
+
+    # reset
+    randomize_apply_external_force_torque = EventTerm(
+        func=mdp.apply_external_force_torque,
+        mode="reset",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "force_range": (-10.0, 10.0),
+            "torque_range": (-10.0, 10.0),
+        },
+    )
+
+    randomize_reset_joints = EventTerm(
+        func=mdp.reset_joints_by_scale,
+        # func=mdp.reset_joints_by_offset,
+        mode="reset",
+        params={
+            "position_range": (1.0, 1.0),
+            "velocity_range": (0.0, 0.0),
+        },
+    )
+
+    randomize_actuator_gains = EventTerm(
+        func=mdp.randomize_actuator_gains,
+        mode="reset",
+        params={
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+            "stiffness_distribution_params": (0.85, 1.15),
+            "damping_distribution_params": (0.85, 1.15),
+            "operation": "scale",
+            "distribution": "uniform",
+        },
+    )
+
+    randomize_reset_base = EventTerm(
+        func=mdp.reset_root_state_uniform,
+        mode="reset",
+        params={
+            "pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "yaw": (-3.14, 3.14)},
+            "velocity_range": {
+                "x": (-0.5, 0.5),
+                "y": (-0.5, 0.5),
+                "z": (-0.5, 0.5),
+                "roll": (-0.5, 0.5),
+                "pitch": (-0.5, 0.5),
+                "yaw": (-0.5, 0.5),
+            },
+        },
+    )
+
+    # interval
+    randomize_push_robot = EventTerm(
+        func=mdp.push_by_setting_velocity,
+        mode="interval",
+        interval_range_s=(10.0, 15.0),
+        params={"velocity_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5)}},
+    )
+
+
+@configclass
+class RewardsCfg:
+    """Reward terms for the MDP."""
+
+    # General
+    is_terminated = RewTerm(func=mdp.is_terminated, weight=0.0)
+
+    # Root penalties
+    lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=0.0)
+    ang_vel_xy_l2 = RewTerm(func=mdp.ang_vel_xy_l2, weight=0.0)
+    flat_orientation_l2 = RewTerm(func=mdp.flat_orientation_l2, weight=0.0)
+    roll_l2 = RewTerm(func=mdp.roll_l2, weight=0.0)
+    pitch_l2 = RewTerm(func=mdp.pitch_l2, weight=0.0)
+    base_height_l2 = RewTerm(
+        func=mdp.base_height_l2,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "sensor_cfg": SceneEntityCfg("height_scanner_base"),
+            "target_height": 0.0,
+        },
+    )
+    body_lin_acc_l2 = RewTerm(
+        func=mdp.body_lin_acc_l2,
+        weight=0.0,
+        params={"asset_cfg": SceneEntityCfg("robot", body_names="")},
+    )
+
+    # Joint penalties
+    joint_torques_l2 = RewTerm(
+        func=mdp.joint_torques_l2, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")}
+    )
+    joint_vel_l2 = RewTerm(
+        func=mdp.joint_vel_l2, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")}
+    )
+    joint_acc_l2 = RewTerm(
+        func=mdp.joint_acc_l2, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")}
+    )
+
+    joint_deviation_l1 = RewTerm(
+            func=mdp.joint_deviation_l1,
+            weight=0.0,
+            params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")},
+        )
+    
+    def create_joint_deviation_l1_rewterm(self, attr_name, weight, joint_names_pattern):
+        rew_term = RewTerm(
+            func=mdp.joint_deviation_l1,
+            weight=weight,
+            params={"asset_cfg": SceneEntityCfg("robot", joint_names=joint_names_pattern)},
+        )
+        setattr(self, attr_name, rew_term)
+
+    joint_pos_limits = RewTerm(
+        func=mdp.joint_pos_limits, weight=0.0, params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")}
+    )
+    joint_vel_limits = RewTerm(
+        func=mdp.joint_vel_limits,
+        weight=0.0,
+        params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*"), "soft_ratio": 1.0},
+    )
+    joint_power = RewTerm(
+        func=mdp.joint_power,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+        },
+    )
+
+    stand_still_without_cmd = RewTerm(
+        func=mdp.stand_still_without_cmd,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "command_threshold": 0.1,
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+        },
+    )
+
+    joint_pos_penalty = RewTerm(
+        func=mdp.joint_pos_penalty,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+            "stand_still_scale": 5.0,
+            "velocity_threshold": 0.5,
+            "command_threshold": 0.1,
+        },
+    )
+
+    hipx_joint_pos_penalty = RewTerm(
+        func=mdp.joint_pos_penalty,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+            "stand_still_scale": 5.0,
+            "velocity_threshold": 0.5,
+            "command_threshold": 0.1,
+        },
+    )
+
+    hipy_joint_pos_penalty = RewTerm(
+        func=mdp.joint_pos_penalty,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+            "stand_still_scale": 5.0,
+            "velocity_threshold": 0.5,
+            "command_threshold": 0.1,
+        },
+    )
+
+    knee_joint_pos_penalty = RewTerm(
+        func=mdp.joint_pos_penalty,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "asset_cfg": SceneEntityCfg("robot", joint_names=".*"),
+            "stand_still_scale": 5.0,
+            "velocity_threshold": 0.5,
+            "command_threshold": 0.1,
+        },
+    )
+
+
+
+
+    wheel_vel_penalty = RewTerm(
+        func=mdp.wheel_vel_penalty,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot", joint_names=""),
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "command_name": "base_velocity",
+            "velocity_threshold": 0.5,
+            "command_threshold": 0.1,
+        },
+    )
+
+    joint_mirror = RewTerm(
+        func=mdp.joint_mirror,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot"),
+            "mirror_joints": [["FR.*", "RL.*"], ["FL.*", "RR.*"]],
+        },
+    )
+
+    action_mirror = RewTerm(
+        func=mdp.action_mirror,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot"),
+            "mirror_joints": [["FR.*", "RL.*"], ["FL.*", "RR.*"]],
+        },
+    )
+
+    action_sync = RewTerm(
+        func=mdp.action_sync,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot"),
+            "joint_groups": [
+                ["FR_hip_joint", "FL_hip_joint", "RL_hip_joint", "RR_hip_joint"],
+                ["FR_thigh_joint", "FL_thigh_joint", "RL_thigh_joint", "RR_thigh_joint"],
+                ["FR_calf_joint", "FL_calf_joint", "RL_calf_joint", "RR_calf_joint"],
+            ],
+        },
+    )
+
+    # Action penalties
+    applied_torque_limits = RewTerm(
+        func=mdp.applied_torque_limits,
+        weight=0.0,
+        params={"asset_cfg": SceneEntityCfg("robot", joint_names=".*")},
+    )
+    action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=0.0)
+    # smoothness_1 = RewTerm(func=mdp.smoothness_1, weight=0.0)  # Same as action_rate_l2
+    # smoothness_2 = RewTerm(func=mdp.smoothness_2, weight=0.0)  # Unvaliable now
+
+    # Contact sensor
+    undesired_contacts = RewTerm(
+        func=mdp.undesired_contacts,
+        weight=0.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "threshold": 1.0,
+        },
+    )
+    contact_forces = RewTerm(
+        func=mdp.contact_forces,
+        weight=0.0,
+        params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=""), "threshold": 100.0},
+    )
+
+    # Velocity-tracking rewards
+    track_lin_vel_xy_exp = RewTerm(
+        func=mdp.track_lin_vel_xy_exp, weight=0.0, params={"command_name": "base_velocity", "std": math.sqrt(0.5)}
+    )
+    track_ang_vel_z_exp = RewTerm(
+        func=mdp.track_ang_vel_z_exp, weight=0.0, params={"command_name": "base_velocity", "std": math.sqrt(0.5)}
+    )
+
+    # Others
+    # feet_air_time = RewTerm(
+    #     func=mdp.feet_air_time,
+    #     weight=0.0,
+    #     params={
+    #         "command_name": "base_velocity",
+    #         "threshold": 0.5,
+    #         "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+    #     },
+    # )
+
+    feet_air_time = RewTerm(
+        func=mdp.feet_air_time_including_ang_z,
+        weight=0.0,
+        params={
+            "command_name": "base_velocity",
+            "threshold": 0.5,
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+        },
+    )
+
+    feet_air_time_variance = RewTerm(
+        func=mdp.feet_air_time_variance_penalty,
+        weight=0,
+        params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names="")},
+    )
+
+    feet_gait = RewTerm(
+        func=mdp.GaitReward,
+        weight=0.0,
+        params={
+            "std": math.sqrt(0.5),
+            "command_name": "base_velocity",
+            "max_err": 0.2,
+            "velocity_threshold": 0.5,
+            "command_threshold": 0.1,
+            "synced_feet_pair_names": (("", ""), ("", "")),
+            "asset_cfg": SceneEntityCfg("robot"),
+            "sensor_cfg": SceneEntityCfg("contact_forces"),
+        },
+    )
+
+    feet_contact = RewTerm(
+        func=mdp.feet_contact,
+        weight=0.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "command_name": "base_velocity",
+            "expect_contact_num": 2,
+        },
+    )
+
+    feet_contact_without_cmd = RewTerm(
+        func=mdp.feet_contact_without_cmd,
+        weight=0.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "command_name": "base_velocity",
+        },
+    )
+
+    feet_stumble = RewTerm(
+        func=mdp.feet_stumble,
+        weight=0.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+        },
+    )
+
+    feet_slide = RewTerm(
+        func=mdp.feet_slide,
+        weight=0.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_forces", body_names=""),
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+        },
+    )
+
+    stand_still = RewTerm(
+        func=mdp.stand_still_joint_deviation_l1,
+        weight=0,
+        params={
+            "command_name": "base_velocity",
+            "asset_cfg": SceneEntityCfg("robot", joint_names=""),
+        },
+    ) # negetive
+
+    feet_height = RewTerm(
+        func=mdp.feet_height,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "tanh_mult": 2.0,
+            "target_height": 0.05,
+            "command_name": "base_velocity",
+        },
+    )
+
+    feet_height_body = RewTerm(
+        func=mdp.feet_height_body,
+        weight=0.0,
+        params={
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "tanh_mult": 2.0,
+            "target_height": -0.3,
+            "command_name": "base_velocity",
+        },
+    )
+
+    feet_distance_y_exp = RewTerm(
+        func=mdp.feet_distance_y_exp,
+        weight=0.0,
+        params={
+            "std": math.sqrt(0.25),
+            "asset_cfg": SceneEntityCfg("robot", body_names=""),
+            "stance_width": float,
+        },
+    )
+
+    # feet_distance_xy_exp = RewTerm(
+    #     func=mdp.feet_distance_xy_exp,
+    #     weight=0.0,
+    #     params={
+    #         "std": math.sqrt(0.25),
+    #         "asset_cfg": SceneEntityCfg("robot", body_names=""),
+    #         "stance_length": float,
+    #         "stance_width": float,
+    #     },
+    # )
+
+    upward = RewTerm(func=mdp.upward, weight=0.0)
+
+    # lin_vel_xy_l2_with_ang_z_command = RewTerm(
+    #     func=mdp.lin_vel_xy_l2_with_ang_z_command,
+    #     weight=0,
+    #     params={
+    #         "command_name": "base_velocity",
+    #         "command_threshold": 0.1,
+    #     },
+    # ) # negetive
+
+
+@configclass
+class TerminationsCfg:
+    """Termination terms for the MDP."""
+
+    # MDP terminations
+    time_out = DoneTerm(func=mdp.time_out, time_out=True)
+    # command_resample
+    terrain_out_of_bounds = DoneTerm(
+        func=mdp.terrain_out_of_bounds,
+        params={"asset_cfg": SceneEntityCfg("robot"), "distance_buffer": 3.0},
+        time_out=True,
+    )
+
+    # Contact sensor
+    illegal_contact = DoneTerm(
+        func=mdp.illegal_contact,
+        params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=""), "threshold": 1.0},
+    )
+
+    bad_orientation_2 = DoneTerm(func=mdp.bad_orientation_2)
+    
+
+
+
+
+@configclass
+class CurriculumCfg:
+    """Curriculum terms for the MDP."""
+
+    terrain_levels = CurrTerm(func=mdp.terrain_levels_vel)
+
+    command_levels = CurrTerm(
+        func=mdp.command_levels_vel,
+        params={
+            "reward_term_name": "track_lin_vel_xy_exp",
+            "range_multiplier": (0.1, 1.0),
+        },
+    )
+
+
+##
+# Environment configuration
+##
+
+
+@configclass
+class LocomotionVelocityRoughEnvCfg(ManagerBasedRLEnvCfg):
+    """Configuration for the locomotion velocity-tracking environment."""
+
+    # Scene settings
+    scene: MySceneCfg = MySceneCfg(num_envs=4096, env_spacing=2.5)
+    # Basic settings
+    observations: ObservationsCfg = ObservationsCfg()
+    actions: ActionsCfg = ActionsCfg()
+    commands: CommandsCfg = CommandsCfg()
+    # MDP settings
+    rewards: RewardsCfg = RewardsCfg()
+    terminations: TerminationsCfg = TerminationsCfg()
+    events: EventCfg = EventCfg()
+    curriculum: CurriculumCfg = CurriculumCfg()
+
+    def __post_init__(self):
+        """Post initialization."""
+        # general settings
+        self.decimation = 4
+        self.episode_length_s = 20.0
+        # simulation settings
+        self.sim.dt = 0.005
+        self.sim.render_interval = self.decimation
+        self.sim.physics_material = self.scene.terrain.physics_material
+        self.sim.physx.gpu_max_rigid_patch_count = 10 * 2**15
+        self.sim.physx.max_position_iteration_count = 4
+        self.sim.physx.max_velocity_iteration_count = 1
+        # update sensor update periods
+        # we tick all the sensors based on the smallest update period (physics update period)
+        if self.scene.height_scanner is not None:
+            self.scene.height_scanner.update_period = self.decimation * self.sim.dt
+        if self.scene.contact_forces is not None:
+            self.scene.contact_forces.update_period = self.sim.dt
+
+        # check if terrain levels curriculum is enabled - if so, enable curriculum for terrain generator
+        # this generates terrains with increasing difficulty and is useful for training
+        if getattr(self.curriculum, "terrain_levels", None) is not None:
+            if self.scene.terrain.terrain_generator is not None:
+                self.scene.terrain.terrain_generator.curriculum = True
+        else:
+            if self.scene.terrain.terrain_generator is not None:
+                self.scene.terrain.terrain_generator.curriculum = False
+
+    def disable_zero_weight_rewards(self):
+        """If the weight of rewards is 0, set rewards to None"""
+        for attr in dir(self.rewards):
+            if not attr.startswith("__"):
+                reward_attr = getattr(self.rewards, attr)
+                if not callable(reward_attr) and reward_attr.weight == 0:
+                    setattr(self.rewards, attr, None)
+
+
+def create_obsgroup_class(class_name, terms, enable_corruption=False, concatenate_terms=True):
+    """
+    Dynamically create and register a ObsGroup class based on the given configuration terms.
+
+    :param class_name: Name of the configuration class.
+    :param terms: Configuration terms, a dictionary where keys are term names and values are term content.
+    :param enable_corruption: Whether to enable corruption for the observation group. Defaults to False.
+    :param concatenate_terms: Whether to concatenate the observation terms in the group. Defaults to True.
+    :return: The dynamically created class.
+    """
+    # Dynamically determine the module name
+    module_name = inspect.getmodule(inspect.currentframe()).__name__
+
+    # Define the post-init function
+    def post_init_wrapper(self):
+        setattr(self, "enable_corruption", enable_corruption)
+        setattr(self, "concatenate_terms", concatenate_terms)
+
+    # Dynamically create the class using ObsGroup as the base class
+    terms["__post_init__"] = post_init_wrapper
+    dynamic_class = configclass(type(class_name, (ObsGroup,), terms))
+
+    # Custom serialization and deserialization
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+
+    # Add custom serialization methods to the class
+    dynamic_class.__getstate__ = __getstate__
+    dynamic_class.__setstate__ = __setstate__
+
+    # Place the class in the global namespace for accessibility
+    globals()[class_name] = dynamic_class
+
+    # Register the dynamic class in the module's dictionary
+    if module_name in sys.modules:
+        sys.modules[module_name].__dict__[class_name] = dynamic_class
+    else:
+        raise ImportError(f"Module {module_name} not found.")
+
+    # Return the class for external instantiation
+    return dynamic_class
diff --git a/source/rl_training/rl_training/ui_extension_example.py b/source/rl_training/rl_training/ui_extension_example.py
new file mode 100644
index 0000000..90dd2cb
--- /dev/null
+++ b/source/rl_training/rl_training/ui_extension_example.py
@@ -0,0 +1,52 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import omni.ext
+
+
+# Functions and vars are available to other extension as usual in python: `example.python_ext.some_public_function(x)`
+def some_public_function(x: int):
+    print("[rl_training] some_public_function was called with x: ", x)
+    return x**x
+
+
+# Any class derived from `omni.ext.IExt` in top level module (defined in `python.modules` of `extension.toml`) will be
+# instantiated when extension gets enabled and `on_startup(ext_id)` will be called. Later when extension gets disabled
+# on_shutdown() is called.
+class ExampleExtension(omni.ext.IExt):
+    # ext_id is current extension id. It can be used with extension manager to query additional information, like where
+    # this extension is located on filesystem.
+    def on_startup(self, ext_id):
+        print("[rl_training] startup")
+
+        self._count = 0
+
+        self._window = omni.ui.Window("My Window", width=300, height=300)
+        with self._window.frame:
+            with omni.ui.VStack():
+                label = omni.ui.Label("")
+
+                def on_click():
+                    self._count += 1
+                    label.text = f"count: {self._count}"
+
+                def on_reset():
+                    self._count = 0
+                    label.text = "empty"
+
+                on_reset()
+
+                with omni.ui.HStack():
+                    omni.ui.Button("Add", clicked_fn=on_click)
+                    omni.ui.Button("Reset", clicked_fn=on_reset)
+
+    def on_shutdown(self):
+        print("[rl_training] shutdown")
diff --git a/source/rl_training/setup.py b/source/rl_training/setup.py
new file mode 100644
index 0000000..1060a6c
--- /dev/null
+++ b/source/rl_training/setup.py
@@ -0,0 +1,55 @@
+# Copyright (c) 2025 Deep Robotics
+# SPDX-License-Identifier: BSD 3-Clause
+# 
+# # Copyright (c) 2024-2025 Ziqi Fan
+# SPDX-License-Identifier: Apache-2.0
+
+"""Installation script for the 'rl_training' python package."""
+
+import os
+import toml
+
+from setuptools import setup
+
+# Obtain the extension data from the extension.toml file
+EXTENSION_PATH = os.path.dirname(os.path.realpath(__file__))
+# Read the extension.toml file
+EXTENSION_TOML_DATA = toml.load(os.path.join(EXTENSION_PATH, "config", "extension.toml"))
+
+# Minimum dependencies required prior to installation
+INSTALL_REQUIRES = [
+    # base
+    "psutil",
+    "colorama",
+    "xacrodoc",
+    # amp
+    "numpy",
+    "pandas",
+    "pinocchio",
+    # rl
+    "cusrl[all]",
+]
+
+# Installation operation
+setup(
+    name="rl_training",
+    packages=["rl_training"],
+    author=EXTENSION_TOML_DATA["package"]["author"],
+    maintainer=EXTENSION_TOML_DATA["package"]["maintainer"],
+    url=EXTENSION_TOML_DATA["package"]["repository"],
+    version=EXTENSION_TOML_DATA["package"]["version"],
+    description=EXTENSION_TOML_DATA["package"]["description"],
+    keywords=EXTENSION_TOML_DATA["package"]["keywords"],
+    install_requires=INSTALL_REQUIRES,
+    license="Apache License 2.0",
+    include_package_data=True,
+    python_requires=">=3.10",
+    classifiers=[
+        "Natural Language :: English",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Isaac Sim :: 4.5.0",
+        "Isaac Sim :: 5.0.0",
+    ],
+    zip_safe=False,
+)