281 lines
10 KiB
Python
281 lines
10 KiB
Python
# Copyright (c) 2025 Deep Robotics
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# SPDX-License-Identifier: BSD 3-Clause
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# Copyright (c) 2024-2025 Ziqi Fan
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# SPDX-License-Identifier: Apache-2.0
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# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
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# All rights reserved.
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#
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# SPDX-License-Identifier: Apache-2.0
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"""Script to play a checkpoint if an RL agent from RSL-RL."""
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"""Launch Isaac Sim Simulator first."""
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import argparse
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import os
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import sys
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PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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PROJECT_SOURCE_PATH = os.path.join(PROJECT_ROOT, "source", "rl_training")
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if PROJECT_SOURCE_PATH not in sys.path:
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sys.path.insert(0, PROJECT_SOURCE_PATH)
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from isaaclab.app import AppLauncher
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# local imports
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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import cli_args
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from rl_utils import camera_follow
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# add argparse arguments
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parser = argparse.ArgumentParser(description="Train an RL agent with RSL-RL.")
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parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
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parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
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parser.add_argument(
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"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
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)
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parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
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parser.add_argument("--task", type=str, default=None, help="Name of the task.")
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parser.add_argument(
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"--agent", type=str, default="rsl_rl_cfg_entry_point", help="Name of the RL agent configuration entry point."
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)
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parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
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parser.add_argument(
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"--use_pretrained_checkpoint",
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action="store_true",
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help="Use the pre-trained checkpoint from Nucleus.",
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)
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parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
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parser.add_argument("--keyboard", action="store_true", default=False, help="Whether to use keyboard.")
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parser.add_argument(
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"--visual-only",
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action="store_true",
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default=False,
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help="Display the environment with zero actions without loading an RL checkpoint.",
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)
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# append RSL-RL cli arguments
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cli_args.add_rsl_rl_args(parser)
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# append AppLauncher cli args
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AppLauncher.add_app_launcher_args(parser)
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# parse the arguments
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args_cli, hydra_args = parser.parse_known_args()
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# always enable cameras to record video
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if args_cli.video:
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args_cli.enable_cameras = True
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# clear out sys.argv for Hydra
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sys.argv = [sys.argv[0]] + hydra_args
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# launch omniverse app
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app_launcher = AppLauncher(args_cli)
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simulation_app = app_launcher.app
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"""Rest everything follows."""
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import gymnasium as gym
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import time
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import torch
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from rsl_rl.runners import OnPolicyRunner
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from isaaclab.devices import Se2Keyboard, Se2KeyboardCfg
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from isaaclab.envs import (
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DirectMARLEnv,
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DirectMARLEnvCfg,
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DirectRLEnvCfg,
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ManagerBasedRLEnvCfg,
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multi_agent_to_single_agent,
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)
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from isaaclab.managers import ObservationTermCfg as ObsTerm
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from isaaclab.utils.assets import retrieve_file_path
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from isaaclab.utils.dict import print_dict
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from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint
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from isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper, export_policy_as_jit, export_policy_as_onnx
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from isaaclab_tasks.utils import get_checkpoint_path
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from isaaclab_tasks.utils.hydra import hydra_task_config
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import rl_training
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import rl_training.tasks # noqa: F401
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print(f"[INFO] Using rl_training package from: {rl_training.__file__}")
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@hydra_task_config(args_cli.task, args_cli.agent)
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def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: RslRlOnPolicyRunnerCfg):
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"""Play with RSL-RL agent."""
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task_name = args_cli.task.split(":")[-1]
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# override configurations with non-hydra CLI arguments
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agent_cfg = cli_args.update_rsl_rl_cfg(agent_cfg, args_cli)
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env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else 50
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# set the environment seed
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# note: certain randomizations occur in the environment initialization so we set the seed here
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env_cfg.seed = agent_cfg.seed
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env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
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# spawn the robot randomly in the grid (instead of their terrain levels)
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env_cfg.scene.terrain.max_init_terrain_level = None
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# reduce the number of terrains to save memory
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if env_cfg.scene.terrain.terrain_generator is not None:
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env_cfg.scene.terrain.terrain_generator.num_rows = 5
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env_cfg.scene.terrain.terrain_generator.num_cols = 5
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env_cfg.scene.terrain.terrain_generator.curriculum = False
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# disable randomization for play
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env_cfg.observations.policy.enable_corruption = False
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# Remove perturbations and parameter randomization for reproducible playback.
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for event_name in (
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"randomize_rigid_body_material",
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"randomize_wheel_material",
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"randomize_support_material",
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"randomize_rigid_body_mass",
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"randomize_rigid_body_mass_base",
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"randomize_rigid_body_inertia",
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"randomize_com_positions",
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"randomize_apply_external_force_torque",
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"randomize_actuator_gains",
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"randomize_push_robot",
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):
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if hasattr(env_cfg.events, event_name):
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setattr(env_cfg.events, event_name, None)
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env_cfg.curriculum.command_levels = None
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if args_cli.keyboard:
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env_cfg.scene.num_envs = 1
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env_cfg.terminations.time_out = None
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env_cfg.commands.base_velocity.debug_vis = False
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config = Se2KeyboardCfg(
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v_x_sensitivity=env_cfg.commands.base_velocity.ranges.lin_vel_x[1]/2,
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v_y_sensitivity=env_cfg.commands.base_velocity.ranges.lin_vel_y[1],
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omega_z_sensitivity=env_cfg.commands.base_velocity.ranges.ang_vel_z[1],
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)
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controller = Se2Keyboard(config)
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env_cfg.observations.policy.velocity_commands = ObsTerm(
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func=lambda env: torch.tensor(controller.advance(), dtype=torch.float32).unsqueeze(0).to(env.device),
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)
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# specify directory for logging experiments
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log_root_path = os.path.join("logs", "rsl_rl", agent_cfg.experiment_name)
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log_root_path = os.path.abspath(log_root_path)
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if args_cli.visual_only:
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resume_path = None
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log_dir = os.path.join(log_root_path, "visual_only")
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print("[INFO] Visual-only mode: no policy checkpoint will be loaded.")
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else:
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print(f"[INFO] Loading experiment from directory: {log_root_path}")
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if args_cli.use_pretrained_checkpoint:
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resume_path = get_published_pretrained_checkpoint("rsl_rl", task_name)
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if not resume_path:
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print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
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return
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elif args_cli.load_run is not None:
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resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
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elif args_cli.checkpoint:
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resume_path = retrieve_file_path(args_cli.checkpoint)
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else:
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resume_path = get_checkpoint_path(log_root_path, agent_cfg.load_run, agent_cfg.load_checkpoint)
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log_dir = os.path.dirname(resume_path)
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# create isaac environment
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env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
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# convert to single-agent instance if required by the RL algorithm
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if isinstance(env.unwrapped, DirectMARLEnv):
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env = multi_agent_to_single_agent(env)
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# wrap for video recording
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if args_cli.video:
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video_kwargs = {
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"video_folder": os.path.join(log_dir, "videos", "play"),
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"step_trigger": lambda step: step == 0,
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"video_length": args_cli.video_length,
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"disable_logger": True,
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}
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print("[INFO] Recording videos during training.")
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print_dict(video_kwargs, nesting=4)
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env = gym.wrappers.RecordVideo(env, **video_kwargs)
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# wrap around environment for rsl-rl
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env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions)
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if args_cli.visual_only:
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policy = None
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else:
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print(f"[INFO]: Loading model checkpoint from: {resume_path}")
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# load previously trained model
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ppo_runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=agent_cfg.device)
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ppo_runner.load(resume_path)
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# obtain the trained policy for inference
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policy = ppo_runner.get_inference_policy(device=env.unwrapped.device)
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# extract the neural network module
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# we do this in a try-except to maintain backwards compatibility.
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try:
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# version 2.3 onwards
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policy_nn = ppo_runner.alg.policy
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except AttributeError:
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# version 2.2 and below
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policy_nn = ppo_runner.alg.actor_critic
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# export policy to onnx/jit
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export_model_dir = os.path.join(os.path.dirname(resume_path), "exported")
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export_policy_as_onnx(
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policy=policy_nn,
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normalizer=None,
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path=export_model_dir,
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filename="policy.onnx",
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)
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export_policy_as_jit(
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policy=policy_nn,
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normalizer=None,
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path=export_model_dir,
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filename="policy.pt",
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)
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dt = env.unwrapped.step_dt
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# print(dt, "dt")
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# reset environment
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obs = env.get_observations()
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timestep = 0
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# simulate environment
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while simulation_app.is_running():
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start_time = time.time()
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# run everything in inference mode
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with torch.inference_mode():
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# agent stepping
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if policy is None:
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actions = torch.zeros((env.num_envs, env.num_actions), device=env.unwrapped.device)
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else:
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actions = policy(obs)
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# env stepping
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obs, _, _, _ = env.step(actions)
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if args_cli.video:
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timestep += 1
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# Exit the play loop after recording one video
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if timestep == args_cli.video_length:
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break
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if args_cli.keyboard:
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camera_follow(env)
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# time delay for real-time evaluation
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sleep_time = dt - (time.time() - start_time)
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if args_cli.real_time and sleep_time > 0:
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time.sleep(sleep_time)
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# close the simulator
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env.close()
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if __name__ == "__main__":
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# run the main function
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main()
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# close sim app
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simulation_app.close()
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