Add eval_kit evaluation suite with WebRTC visualization
- eval_kit/core: 零框架依赖的指标内核（Trace/metrics/aggregation/terrain_spec/report/naming） - eval_kit/isaaclab: 适配层（env_cfg 改造、rollout 采集、checkpoint 加载） - eval_kit/profiles.py / protocols.py: 机器人形态描述与评估口径预设 - eval_kit/tests: 24 项内核自检 + 21 项命令行自检（都不需要仿真） - eval_suite.py: 编排入口，每个（阶段, 种子）一个子进程；与 eval.py 指标口径一致 - WebRTC 可视化：--livestream 透传、视口相机跟随/自由（--view-env / --free-camera）、 连接前停顿（--pause-s）、自动放开客户端改画幅、打印连接地址 - 诊断与替代路径：rollout 进度打印、--record-frames 抓帧
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# Copyright (c) 2025 Deep Robotics
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# SPDX-License-Identifier: BSD 3-Clause
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"""指标计算：纯函数，只依赖 numpy。
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输入是 :class:`~eval_kit.core.schema.Trace`、:class:`~eval_kit.core.schema.RobotProfile`
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和 :class:`~eval_kit.core.schema.EvalProtocol`，输出是一个扁平的指标字典。
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指标口径与 ``scripts/reinforcement_learning/rsl_rl/eval.py`` 保持一致（同样的取点、
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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
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有意为之：
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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），
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传入的 ``profile.mass_kg`` 只作兜底。
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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。
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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
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「支撑裕度 = 0.00」，看起来像测出来的值，实际是没测。
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"""
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from __future__ import annotations
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from typing import Any
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import numpy as np
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from eval_kit.core.schema import EvalProtocol, RobotProfile, Trace
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#: 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）。
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_ALL_FRAMES = slice(None)
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#: 动作执行之后的帧，用于逐帧累积类指标。
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_STEP_FRAMES = slice(1, None)
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_GRAVITY = 9.81
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def tilt_from_projected_gravity(projected_gravity_b: np.ndarray) -> np.ndarray:
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"""由机体系投影重力算倾角 [rad]。
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``projected_gravity_b`` 为 ``(T, N, 3)``，返回 ``(T, N)``。
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"""
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z_axis = np.clip(-projected_gravity_b[..., 2], -1.0, 1.0)
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return np.arccos(z_axis)
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def support_margin_xy(root_xy: np.ndarray, foot_xy: np.ndarray) -> np.ndarray:
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"""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。
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``root_xy`` 为 ``(N, 2)``，``foot_xy`` 为 ``(N, F, 2)``，返回 ``(N,)``。
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这是当前实现采用的口径（与旧 ``eval.py`` 相同），记录在
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``EvalProtocol.stability_margin_method`` 里。它比凸包/ZMP 版本粗糙，
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但不会在退化多边形上失败，也不需要 scipy。
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"""
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min_xy = np.min(foot_xy, axis=1)
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max_xy = np.max(foot_xy, axis=1)
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inside_x = (root_xy[:, 0] >= min_xy[:, 0]) & (root_xy[:, 0] <= max_xy[:, 0])
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inside_y = (root_xy[:, 1] >= min_xy[:, 1]) & (root_xy[:, 1] <= max_xy[:, 1])
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inside = inside_x & inside_y
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inside_dist = np.minimum(
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np.minimum(root_xy[:, 0] - min_xy[:, 0], max_xy[:, 0] - root_xy[:, 0]),
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np.minimum(root_xy[:, 1] - min_xy[:, 1], max_xy[:, 1] - root_xy[:, 1]),
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)
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outside_dx = np.clip(np.maximum(min_xy[:, 0] - root_xy[:, 0], root_xy[:, 0] - max_xy[:, 0]), 0.0, None)
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outside_dy = np.clip(np.maximum(min_xy[:, 1] - root_xy[:, 1], root_xy[:, 1] - max_xy[:, 1]), 0.0, None)
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outside_dist = np.sqrt(outside_dx**2 + outside_dy**2)
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return np.where(inside, inside_dist, -outside_dist)
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def _nanmean_or_nan(values: np.ndarray) -> float:
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"""均值；空数组返回 nan（而不是 0，也不是 numpy 的警告）。"""
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if values.size == 0:
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return float("nan")
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return float(np.mean(values))
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def compute_metrics(
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trace: Trace,
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profile: RobotProfile,
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protocol: EvalProtocol,
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*,
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include_per_env: bool = False,
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) -> dict[str, Any]:
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"""由一次 rollout 的轨迹算出全部指标。
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指标名的命名与旧 ``eval.py`` 完全一致，便于新旧结果直接对照。
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"""
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num_steps = trace.num_steps
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num_envs = trace.num_envs
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dt = float(trace.dt)
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if abs(protocol.dt - dt) > 0.01 * dt:
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raise ValueError(
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f"协议 dt={protocol.dt} 与轨迹 dt={dt} 不一致。"
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"通常意味着协议里的步长与任务的 decimation 不匹配。"
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)
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command = np.asarray(trace.command, dtype=np.float64)
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command_xy = command[:2]
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command_yaw = float(command[2])
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command_dir = command_xy.copy()
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norm = float(np.linalg.norm(command_dir))
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command_dir = command_dir / norm if norm > 1e-6 else np.array([1.0, 0.0])
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# ------------------------------------------------------------------ 成功判定
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base_xy = np.asarray(trace.base_pos_w, dtype=np.float64)[..., :2]
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delta_xy = base_xy - base_xy[0][None, :, :]
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progress = np.einsum("tnc,c->tn", delta_xy[_ALL_FRAMES], command_dir)
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max_progress = progress.max(axis=0)
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reached = max_progress >= protocol.success_distance_m
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tilt = tilt_from_projected_gravity(np.asarray(trace.projected_gravity_b, dtype=np.float64))
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tilt_steps = tilt[_STEP_FRAMES]
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max_tilt = tilt_steps.max(axis=0)
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ever_fallen = max_tilt > protocol.tilt_threshold_rad
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success = reached & ~ever_fallen
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success_count = int(np.count_nonzero(success))
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# 首次达成成功的时刻：第 i 帧（i>=1）达成则记 i*dt。
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crossed = progress[_STEP_FRAMES] >= protocol.success_distance_m
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complete_time = np.full((num_envs,), np.nan, dtype=np.float64)
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for env_index in range(num_envs):
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hit = np.flatnonzero(crossed[:, env_index])
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if hit.size:
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complete_time[env_index] = float(hit[0] + 1) * dt
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successful_time = complete_time[success & np.isfinite(complete_time)]
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avg_time = _nanmean_or_nan(successful_time)
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# ------------------------------------------------------------- 速度跟踪误差
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lin_vel_b = np.asarray(trace.base_lin_vel_b, dtype=np.float64)[_STEP_FRAMES]
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ang_vel_b = np.asarray(trace.base_ang_vel_b, dtype=np.float64)[_STEP_FRAMES]
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lin_error = np.linalg.norm(lin_vel_b[..., :2] - command_xy[None, None, :], axis=-1)
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yaw_error = np.abs(ang_vel_b[..., 2] - command_yaw)
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# -------------------------------------------------------------- 能耗与路程
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joint_torque = np.asarray(trace.joint_torque, dtype=np.float64)[_STEP_FRAMES]
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joint_vel = np.asarray(trace.joint_vel, dtype=np.float64)[_STEP_FRAMES]
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mechanical_power = joint_torque * joint_vel
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if protocol.energy_convention == "absolute":
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integrand = np.abs(mechanical_power)
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elif protocol.energy_convention == "signed":
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integrand = mechanical_power
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else:
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raise ValueError(
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f"未知能耗约定 {protocol.energy_convention!r}；可选 'absolute' 或 'signed'。"
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)
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energy = integrand.sum(axis=(0, 2)) * dt
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lin_vel_w = np.asarray(trace.base_lin_vel_w, dtype=np.float64)
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route = np.linalg.norm(lin_vel_w[_STEP_FRAMES][..., :2], axis=-1).sum(axis=0) * dt
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# ------------------------------------------------------------ 运动不稳定性
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accel = np.linalg.norm(np.diff(lin_vel_w, axis=0), axis=-1) / dt
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instability_count = (accel > protocol.acc_threshold_mps2).sum(axis=0)
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stable_time = (tilt_steps < protocol.tilt_threshold_rad).sum(axis=0) * dt
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# ------------------------------------------------------------- 支撑多边形
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feet_pos_w = np.asarray(trace.feet_pos_w, dtype=np.float64)[_STEP_FRAMES]
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num_feet = int(feet_pos_w.shape[2])
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if num_feet > 0:
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margins = support_margin_xy(base_xy[_STEP_FRAMES][:, :, :].reshape(-1, 2), feet_pos_w.reshape(-1, num_feet, 2))
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margins = margins.reshape(feet_pos_w.shape[0], num_envs)
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avg_margin = _nanmean_or_nan(margins)
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margin_method = protocol.stability_margin_method
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else:
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avg_margin = float("nan")
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margin_method = "unavailable"
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# --------------------------------------------------------------- 支撑接触
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support_force = np.asarray(trace.support_force_n, dtype=np.float64)
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support_contact_time = np.asarray(trace.support_contact_time_s, dtype=np.float64)
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num_support_bodies = int(support_force.shape[2])
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support_threshold = (
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profile.support_force_threshold_n
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if protocol.support_force_threshold_n is None
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else protocol.support_force_threshold_n
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)
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if num_support_bodies > 0:
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active = np.any(support_force[_STEP_FRAMES] > support_threshold, axis=2) # (T-1, N)
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support_steps = active.sum(axis=0)
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# 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）。
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active_padded = np.concatenate([np.zeros((1, num_envs), dtype=bool), active], axis=0)
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support_events = (active_padded[1:] & ~active_padded[:-1]).sum(axis=0)
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max_support_time = support_contact_time[_STEP_FRAMES].max(axis=(0, 2))
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support_contact_ratio = _nanmean_or_nan(support_steps / max(num_steps, 1))
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avg_support_events = float(np.mean(support_events))
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avg_max_support_time = float(np.mean(max_support_time))
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if success_count > 0:
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success_support_ratio = float(np.mean((support_events[success] > 0).astype(np.float64)))
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else:
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success_support_ratio = float("nan")
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else:
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support_contact_ratio = float("nan")
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avg_support_events = float("nan")
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avg_max_support_time = float("nan")
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success_support_ratio = float("nan")
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# --------------------------------------------------------- 质量与 COT
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mass = trace.mass_kg if trace.mass_kg is not None else profile.mass_kg
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if mass is None or mass <= 0.0:
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cost_of_transport = np.full((num_envs,), np.nan)
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mass_used = float("nan")
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else:
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cost_of_transport = energy / ((route + 1e-6) * mass * _GRAVITY)
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mass_used = float(mass)
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metrics: dict[str, Any] = {
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"profile": profile.name,
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"protocol_fingerprint": protocol.fingerprint(),
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"num_envs": float(num_envs),
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"num_steps": float(num_steps),
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"episode_length_s": float(num_steps * dt),
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"terrain_size_m": float(protocol.terrain_size_m),
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"success_distance_m": float(protocol.success_distance_m),
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"robot_mass_kg": mass_used,
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"energy_convention": protocol.energy_convention,
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"stability_margin_method": margin_method,
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"tilt_threshold_rad": float(protocol.tilt_threshold_rad),
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"acc_threshold_mps2": float(protocol.acc_threshold_mps2),
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"support_force_threshold_n": float(support_threshold) if num_support_bodies > 0 else float("nan"),
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# --- 成功率与进度
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"success_count": float(success_count),
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"success_rate": float(success_count / max(num_envs, 1)),
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"avg_final_progress_m": float(np.mean(max_progress)),
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# --- 时间
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"avg_time_consumption_s": avg_time,
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"time_efficiency": float(
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avg_time / (protocol.success_distance_m / max(abs(float(command[0])), 1e-6))
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)
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if np.isfinite(avg_time)
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else float("nan"),
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# --- 跟踪误差
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"mean_lin_vel_error": float(np.mean(lin_error)),
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"mean_yaw_vel_error": float(np.mean(yaw_error)),
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# --- 能耗
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"avg_energy_j": float(np.mean(energy)),
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"avg_cost_of_transport": _nanmean_or_nan(cost_of_transport),
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# --- 稳定性
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"unstability_rate": float(np.mean(instability_count / max(num_steps, 1))),
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"avg_stability_margin_m": avg_margin,
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"avg_max_tilt_rad": float(np.mean(max_tilt)),
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"avg_stable_time_s": float(np.mean(stable_time)),
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# --- 支撑接触
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"avg_support_contact_ratio": support_contact_ratio,
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"avg_support_events": avg_support_events,
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"avg_max_support_contact_time_s": avg_max_support_time,
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"successful_runs_with_support_ratio": success_support_ratio,
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}
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if include_per_env:
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metrics["_per_env"] = {
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"success": success.astype(float).tolist(),
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"max_progress": max_progress.tolist(),
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"max_tilt": max_tilt.tolist(),
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}
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return metrics
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