Kuavo机器人mjalb框架配置
ting-zheng
2026-02-27 发布35 浏览 · 2 点赞 · 0 收藏
Leju Kuavo4 机器人配置指南
本文档详细说明如何为 Leju Kuavo4 机器人配置 Velocity 策略和 Mimic/Tracking 策略
目录
1. Kuavo4 机器人概述
1.1 机器人参数
| 参数 | 值 |
|---|---|
| 自由度 (DOF) | 28 |
| 腿部关节 | 12 (每侧6个: hip_roll/yaw/pitch, knee, ankle_pitch/roll) |
| 手臂关节 | 14 (每侧7个: arm_pitch/roll/yaw, elbow, hand_yaw/roll/pitch) |
| 腰部关节 | 无 |
| 头部关节 | 2 (zhead_1, zhead_2) |
| 站立高度 | ~0.82m |
| 基座名称 | base_link |
1.2 关节命名规则
Kuavo4 使用以下关节命名规则：
腿部：
leg_l1_joint ~ leg_r1_joint : 左/右髋 roll
leg_l2_joint ~ leg_r2_joint : 左/右髋 yaw
leg_l3_joint ~ leg_r3_joint : 左/右髋 pitch
leg_l4_joint ~ leg_r4_joint : 左/右膝
leg_l5_joint ~ leg_r5_joint : 左/右踝 pitch
leg_l6_joint ~ leg_r6_joint : 左/右踝 roll
手臂：
zarm_l1_joint ~ zarm_r1_joint : 左/右肩 pitch
zarm_l2_joint ~ zarm_r2_joint : 左/右臂 roll
zarm_l3_joint ~ zarm_r3_joint : 左/右臂 yaw
zarm_l4_joint ~ zarm_r4_joint : 左/右肘
zarm_l5_joint ~ zarm_r5_joint : 左/手腕 yaw
zarm_l6_joint ~ zarm_r6_joint : 左/手腕 roll
zarm_l7_joint ~ zarm_r7_joint : 左/手腕 pitch
头部：
zhead_1_joint : 头 yaw
zhead_2_joint : 头 pitch
1.3 关键链接名称
| 链接 | 名称 |
|---|---|
| 躯干 | base_link |
| 左髋 | leg_l1_link |
| 右髋 | leg_r1_link |
| 左膝 | leg_l4_link |
| 右膝 | leg_r4_link |
| 左踝 | leg_l6_link |
| 右踝 | leg_r6_link |
| 左肩 | zarm_l1_link |
| 右肩 | zarm_r1_link |
| 左肘 | zarm_l4_link |
| 右肘 | zarm_r4_link |
| 左腕 | zarm_l7_link |
| 右腕 | zarm_r7_link |
1.4 传感器配置
| 传感器 | 名称 | 用途 |
|---|---|---|
| 陀螺仪 | BodyGyro |
角速度 |
| 速度计 | BodyVel |
线速度 |
2. 文件结构
unitree_rl_mjlab/
├── mjlab/
│ ├── asset_zoo/
│ │ └── robots/
│ │ └── leju_kuavo4/
│ │ ├── xml/
│ │ │ └── biped_s100045.xml # MuJoCo模型文件
│ │ ├── kuavo4_constants.py # 机器人常量配置
│ │ └── meshes/ # 网格文件
│ │
│ ├── tasks/
│ │ ├── velocity/config/leju_kuavo4/
│ │ │ ├── __init__.py # Velocity任务注册
│ │ │ ├── env_cfgs.py # Velocity环境配置
│ │ │ └── rl_cfg.py # Velocity RL配置
│ │ │
│ │ └── tracking/config/leju_kuavo4/
│ │ ├── __init__.py # Tracking任务注册
│ │ ├── env_cfgs.py # Tracking环境配置
│ │ └── rl_cfgs.py # Tracking RL配置
│ │
│ └── motions/
│ └── kuavo4/ # 运动数据目录
│ └── kuavo4_mimic.npz # 转换后的运动数据
│
├── scripts/
│ ├── train.py # 训练脚本
│ ├── play.py # 验证脚本
│ └── csv_to_npz.py # CSV转NPZ工具
3. 机器人资产配置
3.1 kuavo4_constants.py 配置详解
文件位置：mjlab/asset_zoo/robots/leju_kuavo4/kuavo4_constants.py
3.1.1 执行器配置
Kuavo4 的执行器按扭矩限制分为四类：
# ========================================
# Actuator config (kuavo4_constants.py)
# ========================================
# Large motor (180 Nm) - 用于大扭矩关节
# 用于：hip roll (leg_l1/r1), knee (leg_l4/r4)
ROTOR_INERTIAS_LARGE = (0.489e-4, 0.109e-4, 0.738e-4)
GEARS_LARGE = (1, 4.5, 5)
ARMATURE_LARGE = reflected_inertia_from_two_stage_planetary(
ROTOR_INERTIAS_LARGE, GEARS_LARGE
)
# Medium motor (100 Nm) - 用于中等扭矩关节
# 用于：hip yaw/pitch (leg_l2/l3/r2/r3), ankle pitch (leg_l5/r5)
# arm pitch (zarm_l1/r1)
ROTOR_INERTIAS_MEDIUM = (0.489e-4, 0.098e-4, 0.533e-4)
GEARS_MEDIUM = (1, 4.5, 1 + (48 / 22))
ARMATURE_MEDIUM = reflected_inertia_from_two_stage_planetary(
ROTOR_INERTIAS_MEDIUM, GEARS_MEDIUM
)
# Small motor (50 Nm) - 用于小扭矩关节
# 用于：ankle roll (leg_l6/r6), arm roll/yaw (zarm_l2/l3/r2/r3), elbow
ROTOR_INERTIAS_SMALL = (0.139e-4, 0.017e-4, 0.169e-4)
GEARS_SMALL = (1, 1 + (46 / 18), 1 + (56 / 16))
ARMATURE_SMALL = reflected_inertia_from_two_stage_planetary(
ROTOR_INERTIAS_SMALL, GEARS_SMALL
)
# Tiny motor (12 Nm) - 用于微小扭矩关节
# 用于：hand joints (zarm_l5/l6/l7/r5/r6/r7), head (zhead_1/2)
ROTOR_INERTIAS_TINY = (0.068e-4, 0.0, 0.0)
GEARS_TINY = (1, 5, 5)
ARMATURE_TINY = reflected_inertia_from_two_stage_planetary(
ROTOR_INERTIAS_TINY, GEARS_TINY
)
3.1.2 关节执行器定义
# ========================================
# Joint actuator configs
# ========================================
# 腿部执行器
KUAVO_ACTUATOR_HIP_ROLL = BuiltinPositionActuatorCfg(
target_names_expr=(r"leg_[lr]1_joint",), # 匹配 leg_l1_joint, leg_r1_joint
stiffness=STIFFNESS_LARGE,
damping=DAMPING_LARGE,
effort_limit=180.0,
armature=ACTUATOR_LARGE.reflected_inertia,
)
KUAVO_ACTUATOR_HIP_YAW = BuiltinPositionActuatorCfg(
target_names_expr=(r"leg_[lr]2_joint",),
stiffness=STIFFNESS_MEDIUM,
damping=DAMPING_MEDIUM,
effort_limit=100.0,
armature=ACTUATOR_MEDIUM.reflected_inertia,
)
KUAVO_ACTUATOR_HIP_PITCH = BuiltinPositionActuatorCfg(
target_names_expr=(r"leg_[lr]3_joint",),
stiffness=STIFFNESS_MEDIUM,
damping=DAMPING_MEDIUM,
effort_limit=100.0,
armature=ACTUATOR_MEDIUM.reflected_inertia,
)
KUAVO_ACTUATOR_KNEE = BuiltinPositionActuatorCfg(
target_names_expr=(r"leg_[lr]4_joint",),
stiffness=STIFFNESS_LARGE,
damping=DAMPING_LARGE,
effort_limit=180.0,
armature=ACTUATOR_LARGE.reflected_inertia,
)
KUAVO_ACTUATOR_ANKLE_PITCH = BuiltinPositionActuatorCfg(
target_names_expr=(r"leg_[lr]5_joint",),
stiffness=STIFFNESS_SMALL,
damping=DAMPING_SMALL,
effort_limit=50.0,
armature=ACTUATOR_SMALL.reflected_inertia,
)
KUAVO_ACTUATOR_ANKLE_ROLL = BuiltinPositionActuatorCfg(
target_names_expr=(r"leg_[lr]6_joint",),
stiffness=STIFFNESS_SMALL,
damping=DAMPING_SMALL,
effort_limit=50.0,
armature=ACTUATOR_SMALL.reflected_inertia,
)
# 手臂执行器
KUAVO_ACTUATOR_ARM_PITCH = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]1_joint",),
stiffness=STIFFNESS_MEDIUM,
damping=DAMPING_MEDIUM,
effort_limit=100.0,
armature=ACTUATOR_MEDIUM.reflected_inertia,
)
KUAVO_ACTUATOR_ARM_ROLL = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]2_joint",),
stiffness=STIFFNESS_SMALL,
damping=DAMPING_SMALL,
effort_limit=50.0,
armature=ACTUATOR_SMALL.reflected_inertia,
)
KUAVO_ACTUATOR_ARM_YAW = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]3_joint",),
stiffness=STIFFNESS_SMALL,
damping=DAMPING_SMALL,
effort_limit=50.0,
armature=ACTUATOR_SMALL.reflected_inertia,
)
KUAVO_ACTUATOR_ELBOW = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]4_joint",),
stiffness=STIFFNESS_SMALL,
damping=DAMPING_SMALL,
effort_limit=50.0,
armature=ACTUATOR_SMALL.reflected_inertia,
)
KUAVO_ACTUATOR_HAND_YAW = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]5_joint",),
stiffness=STIFFNESS_TINY,
damping=DAMPING_TINY,
effort_limit=12.0,
armature=ACTUATOR_TINY.reflected_inertia,
)
KUAVO_ACTUATOR_HAND_ROLL = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]6_joint",),
stiffness=STIFFNESS_TINY,
damping=DAMPING_TINY,
effort_limit=12.0,
armature=ACTUATOR_TINY.reflected_inertia,
)
KUAVO_ACTUATOR_HAND_PITCH = BuiltinPositionActuatorCfg(
target_names_expr=(r"zarm_[lr]7_joint",),
stiffness=STIFFNESS_TINY,
damping=DAMPING_TINY,
effort_limit=12.0,
armature=ACTUATOR_TINY.reflected_inertia,
)
# 头部执行器
KUAVO_ACTUATOR_HEAD_YAW = BuiltinPositionActuatorCfg(
target_names_expr=("zhead_1_joint",),
stiffness=STIFFNESS_TINY,
damping=DAMPING_TINY,
effort_limit=1.5,
armature=ACTUATOR_TINY.reflected_inertia,
)
KUAVO_ACTUATOR_HEAD_PITCH = BuiltinPositionActuatorCfg(
target_names_expr=("zhead_2_joint",),
stiffness=STIFFNESS_TINY,
damping=DAMPING_TINY,
effort_limit=ACTUATOR_TINY.effort_limit,
armature=ACTUATOR_TINY.reflected_inertia,
)
3.1.3 初始姿态配置
# ========================================
# Keyframe config (HOME_KEYFRAME)
# ========================================
HOME_KEYFRAME = EntityCfg.InitialStateCfg(
pos=(0, 0, 0.82), # 站立高度
joint_pos={
# 腿部 - 直立姿态
r"leg_[lr]1_joint": 0.0, # Hip roll
r"leg_[lr]2_joint": 0.0, # Hip yaw
r"leg_[lr]3_joint": -0.1, # Hip pitch
r"leg_[lr]4_joint": 0.3, # Knee
r"leg_[lr]5_joint": -0.15, # Ankle pitch
r"leg_[lr]6_joint": 0.0, # Ankle roll
# 手臂 - 自然下垂姿态
# 注意：roll关节需要左右相反的值
r"zarm_[lr]1_joint": 0.3, # Arm pitch
r"zarm_l2_joint": 0.2, # Left arm roll
r"zarm_r2_joint": -0.2, # Right arm roll (负值！)
r"zarm_[lr]3_joint": 0.0, # Arm yaw
r"zarm_[lr]4_joint": -0.5, # Elbow
r"zarm_[lr]5_joint": 0.0, # Hand yaw
r"zarm_l6_joint": 0.0, # Left wrist roll
r"zarm_r6_joint": 0.0, # Right wrist roll
r"zarm_[lr]7_joint": 0.0, # Hand pitch
# 头部 - 中性位置
r"zhead_[12]_joint": 0.0, # Head joints
},
joint_vel={".*": 0.0},
)
3.1.4 碰撞配置
# ========================================
# Collision config
# ========================================
# 训练模式：只启用脚与地面碰撞（提高效率）
FULL_COLLISION_WITHOUT_SELF = CollisionCfg(
geom_names_expr=(r".*_collision$",),
contype=0, # 禁用自身接触
conaffinity=1, # 只与terrain接触
condim={
r"^(left|right)_foot[1-7]_collision$": 3, # 脚部3D摩擦
r".*_collision$": 1, # 其他1D摩擦
},
priority={
r"^(left|right)_foot[1-7]_collision$": 1, # 脚部高优先级
},
friction={
r"^(left|right)_foot[1-7]_collision$": (0.6,), # 脚部摩擦系数
},
)
# Play模式：启用所有碰撞（包括自碰撞）
FULL_COLLISION = CollisionCfg(
geom_names_expr=(r".*_collision$",),
condim={
r"^(left|right)_foot[1-7]_collision$": 3, # 脚部
r".*_collision$": 1, # 其他
},
priority={
r"^(left|right)_foot[1-7]_collision$": 1,
},
friction={
r"^(left|right)_foot[1-7]_collision$": (0.6,),
},
)
3.1.5 机器人配置函数
# ========================================
# Final config
# ========================================
KUAVO4_ARTICULATION = EntityArticulationInfoCfg(
actuators=(
KUAVO_ACTUATOR_HIP_ROLL,
KUAVO_ACTUATOR_HIP_YAW,
KUAVO_ACTUATOR_HIP_PITCH,
KUAVO_ACTUATOR_KNEE,
KUAVO_ACTUATOR_ANKLE_PITCH,
KUAVO_ACTUATOR_ANKLE_ROLL,
KUAVO_ACTUATOR_ARM_PITCH,
KUAVO_ACTUATOR_ARM_ROLL,
KUAVO_ACTUATOR_ARM_YAW,
KUAVO_ACTUATOR_ELBOW,
KUAVO_ACTUATOR_HAND_YAW,
KUAVO_ACTUATOR_HAND_ROLL,
KUAVO_ACTUATOR_HAND_PITCH,
KUAVO_ACTUATOR_HEAD_YAW,
KUAVO_ACTUATOR_HEAD_PITCH,
),
soft_joint_pos_limit_factor=0.9,
)
def get_kuavo4_robot_cfg() -> EntityCfg:
"""获取Kuavo4机器人配置实例
每次调用返回新实例，避免配置共享导致的变异问题。
"""
return EntityCfg(
init_state=HOME_KEYFRAME,
collisions=(FULL_COLLISION_WITHOUT_SELF,), # 默认训练用
spec_fn=get_spec,
articulation=KUAVO4_ARTICULATION,
)
# 计算action scale
KUAVO4_ACTION_SCALE: dict[str, float] = {}
for a in KUAVO4_ARTICULATION.actuators:
assert isinstance(a, BuiltinPositionActuatorCfg)
e = a.effort_limit
s = a.stiffness
names = a.target_names_expr
assert e is not None
for n in names:
KUAVO4_ACTION_SCALE[n] = 0.25 * e / s
3.2 XML配置要点
文件位置：mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml
3.2.1 Contact配置（自碰撞）
<!-- 在compiler标签后添加contact配置 -->
<contact>
<!-- 排除父子关节间的碰撞检测 -->
<exclude body1="base_link" body2="leg_l1_link" />
<exclude body1="base_link" body2="leg_r1_link" />
<exclude body1="base_link" body2="zarm_l1_link" />
<exclude body1="base_link" body2="zarm_r1_link" />
<exclude body1="base_link" body2="zhead_1_link" />
<!-- 腿部父子关节 -->
<exclude body1="leg_l1_link" body2="leg_l2_link" />
<exclude body1="leg_l2_link" body2="leg_l3_link" />
<exclude body1="leg_l3_link" body2="leg_l4_link" />
<exclude body1="leg_l4_link" body2="leg_l5_link" />
<exclude body1="leg_l5_link" body2="leg_l6_link" />
<exclude body1="leg_r1_link" body2="leg_r2_link" />
<exclude body1="leg_r2_link" body2="leg_r3_link" />
<exclude body1="leg_r3_link" body2="leg_r4_link" />
<exclude body1="leg_r4_link" body2="leg_r5_link" />
<exclude body1="leg_r5_link" body2="leg_r6_link" />
<!-- 手臂父子关节 -->
<exclude body1="zarm_l1_link" body2="zarm_l2_link" />
<exclude body1="zarm_l2_link" body2="zarm_l3_link" />
<exclude body1="zarm_l3_link" body2="zarm_l4_link" />
<exclude body1="zarm_l4_link" body2="zarm_l5_link" />
<exclude body1="zarm_l5_link" body2="zarm_l6_link" />
<exclude body1="zarm_l6_link" body2="zarm_l7_link" />
<exclude body1="zarm_r1_link" body2="zarm_r2_link" />
<exclude body1="zarm_r2_link" body2="zarm_r3_link" />
<exclude body1="zarm_r3_link" body2="zarm_r4_link" />
<exclude body1="zarm_r4_link" body2="zarm_r5_link" />
<exclude body1="zarm_r5_link" body2="zarm_r6_link" />
<exclude body1="zarm_r6_link" body2="zarm_r7_link" />
<!-- 头部父子关节 -->
<exclude body1="zhead_1_link" body2="zhead_2_link" />
</contact>
3.2.2 传感器配置
<!-- IMU传感器 -->
<gyro name="BodyGyro" site="imu_site"/>
<accelerometer name="BodyAcc" site="imu_site"/>
<velocimeter name="BodyVel" site="root_site"/>
4. Velocity 策略配置
4.1 环境配置 (env_cfgs.py)
文件位置：mjlab/tasks/velocity/config/leju_kuavo4/env_cfgs.py
"""Leju Kuavo4 velocity environment configurations."""
from mjlab.asset_zoo.robots import (
KUAVO4_ACTION_SCALE,
get_kuavo4_robot_cfg,
)
from mjlab.asset_zoo.robots.leju_kuavo4.kuavo4_constants import FULL_COLLISION
from mjlab.envs import ManagerBasedRlEnvCfg
from mjlab.envs import mdp as envs_mdp
from mjlab.envs.mdp.actions import JointPositionActionCfg
from mjlab.managers.event_manager import EventTermCfg
from mjlab.managers.reward_manager import RewardTermCfg
from mjlab.sensor import ContactMatch, ContactSensorCfg
from mjlab.tasks.velocity import mdp
from mjlab.tasks.velocity.mdp import UniformVelocityCommandCfg
from mjlab.tasks.velocity.velocity_env_cfg import make_velocity_env_cfg
def Leju_kuavo4_rough_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
"""Create Leju Kuavo4 rough terrain velocity configuration.
Args:
play: 如果为True，使用FULL_COLLISION启用自碰撞
"""
cfg = make_velocity_env_cfg()
# Play模式使用FULL_COLLISION，训练模式使用默认配置
if play:
robot_cfg = get_kuavo4_robot_cfg()
robot_cfg.collisions = (FULL_COLLISION,)
cfg.scene.entities = {"robot": robot_cfg}
else:
cfg.scene.entities = {"robot": get_kuavo4_robot_cfg()}
# ========================================
# 传感器配置
# ========================================
# Kuavo4没有foot sites，使用body模式
feet_ground_cfg = ContactSensorCfg(
name="feet_ground_contact",
primary=ContactMatch(
mode="subtree",
pattern=r"^(leg_l6_link|leg_r6_link)$", # 踝关节body
entity="robot",
),
secondary=ContactMatch(mode="body", pattern="terrain"),
fields=("found", "force"),
reduce="netforce",
num_slots=1,
track_air_time=True,
)
self_collision_cfg = ContactSensorCfg(
name="self_collision",
primary=ContactMatch(mode="subtree", pattern="base_link", entity="robot"),
secondary=ContactMatch(mode="subtree", pattern="base_link", entity="robot"),
fields=("found",),
reduce="none",
num_slots=1,
)
cfg.scene.sensors = (feet_ground_cfg, self_collision_cfg)
# 地形课程学习
if cfg.scene.terrain is not None and cfg.scene.terrain.terrain_generator is not None:
cfg.scene.terrain.terrain_generator.curriculum = True
# ========================================
# Action配置
# ========================================
joint_pos_action = cfg.actions["joint_pos"]
assert isinstance(joint_pos_action, JointPositionActionCfg)
joint_pos_action.scale = KUAVO4_ACTION_SCALE
# ========================================
# Viewer配置
# ========================================
cfg.viewer.body_name = "base_link"
# 速度命令可视化高度
twist_cmd = cfg.commands["twist"]
assert isinstance(twist_cmd, UniformVelocityCommandCfg)
twist_cmd.viz.z_offset = 1.15
# ========================================
# 传感器名称修正（关键！）
# ========================================
# Kuavo4使用BodyGyro而非imu_ang_vel
cfg.observations["policy"].terms["base_ang_vel"].params["sensor_name"] = "robot/BodyGyro"
cfg.observations["critic"].terms["base_lin_vel"].params["sensor_name"] = "robot/BodyVel"
# ========================================
# 事件配置
# ========================================
cfg.events["base_com"].params["asset_cfg"].body_names = ("base_link",)
# 脚部摩擦事件
cfg.events["foot_friction"].params["asset_cfg"].geom_names = r"^(left|right)_foot[1-7]_collision$"
# ========================================
# 奖励函数调整
# ========================================
cfg.rewards["body_ang_vel"].params["asset_cfg"].body_names = ("base_link",)
# 姿态奖励 - 根据Kuavo4关节特点调整
cfg.rewards["pose"].params["std_standing"] = {".*": 0.05}
cfg.rewards["pose"].params["std_walking"] = {
# 下肢 - 放松hip roll允许更宽的侧向步幅
r"leg_[lr]3_joint": 0.5, # Hip pitch
r"leg_[lr]1_joint": 0.2, # Hip roll - 放宽
r"leg_[lr]2_joint": 0.15, # Hip yaw
r"leg_[lr]4_joint": 0.5, # Knee
r"leg_[lr]5_joint": 0.15, # Ankle pitch
r"leg_[lr]6_joint": 0.1, # Ankle roll
# 手臂 - 限制自由度保持稳定
r"zarm_[lr]1_joint": 0.15, # Arm pitch
r"zarm_l2_joint": 0.05, # Left arm roll - 严格
r"zarm_r2_joint": 0.05, # Right arm roll - 严格
r"zarm_[lr]3_joint": 0.08, # Arm yaw - 严格
r"zarm_[lr]4_joint": 0.1, # Elbow
r"zarm_[lr]5_joint": 0.12, # Hand yaw - 严格
r"zarm_[lr]6_joint": 0.12, # Hand roll - 严格
r"zarm_[lr]7_joint": 0.12, # Hand pitch - 严格
# 头部
r"zhead_[12]_joint": 0.3,
}
cfg.rewards["pose"].params["std_running"] = {
# 下肢 - 更宽松的活动范围
r"leg_[lr]3_joint": 0.5,
r"leg_[lr]1_joint": 0.3, # Hip roll - 更宽
r"leg_[lr]2_joint": 0.2,
r"leg_[lr]4_joint": 0.5,
r"leg_[lr]5_joint": 0.2,
r"leg_[lr]6_joint": 0.1,
# 手臂
r"zarm_[lr]1_joint": 0.2,
r"zarm_l2_joint": 0.05,
r"zarm_r2_joint": 0.05,
r"zarm_[lr]3_joint": 0.08,
r"zarm_[lr]4_joint": 0.1,
r"zarm_[lr]5_joint": 0.15,
r"zarm_[lr]6_joint": 0.15,
r"zarm_[lr]7_joint": 0.15,
# 头部
r"zhead_[12]_joint": 0.4,
}
# 脚部奖励 - Kuavo4没有foot sites，使用body名称
cfg.rewards["foot_clearance"].params["asset_cfg"].body_names = ("leg_l6_link", "leg_r6_link")
cfg.rewards["foot_slip"].params["asset_cfg"].body_names = ("leg_l6_link", "leg_r6_link")
# 禁用不可用的奖励（Kuavo4 XML缺少相应传感器）
cfg.rewards.pop("angular_momentum", None)
cfg.rewards.pop("soft_landing", None)
# 自碰撞惩罚
cfg.rewards["self_collisions"] = RewardTermCfg(
func=mdp.self_collision_cost,
weight=-1.0,
params={"sensor_name": self_collision_cfg.name},
)
# 禁用依赖geom名称的事件
cfg.events.pop("body_friction", None)
# ========================================
# Play模式配置
# ========================================
if play:
cfg.episode_length_s = int(1e9) # 无限episode长度
cfg.observations["policy"].enable_corruption = False
cfg.events.pop("push_robot", None)
cfg.events["randomize_terrain"] = EventTermCfg(
func=envs_mdp.randomize_terrain,
mode="reset",
params={},
)
if cfg.scene.terrain is not None:
if cfg.scene.terrain.terrain_generator is not None:
cfg.scene.terrain.terrain_generator.curriculum = False
cfg.scene.terrain.terrain_generator.num_cols = 5
cfg.scene.terrain.terrain_generator.num_rows = 5
cfg.scene.terrain.terrain_generator.border_width = 10.0
return cfg
def Leju_kuavo4_flat_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:
"""Create Leju Kuavo4 flat terrain velocity configuration."""
cfg = Leju_kuavo4_rough_env_cfg(play=play)
# 切换到平面地形
assert cfg.scene.terrain is not None
cfg.scene.terrain.terrain_type = "plane"
cfg.scene.terrain.terrain_generator = None
# 禁用地形课程
assert "terrain_levels" in cfg.curriculum
del cfg.curriculum["terrain_levels"]
return cfg
4.2 RL配置 (rl_cfg.py)
"""RL configuration for Leju Kuavo4 velocity task."""
from mjlab.rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
def Leju_kuavo4_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg:
"""Create RL runner configuration for Leju Kuavo4 velocity task."""
return RslRlOnPolicyRunnerCfg(
policy=RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_obs_normalization=True,
critic_obs_normalization=True,
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,
),
experiment_name="kuavo4_velocity",
save_interval=100,
num_steps_per_env=24,
max_iterations=10001,
)
4.3 任务注册 (init.py)
"""Velocity task registration for Leju Kuavo4."""
from mjlab.tasks.registry import register_mjlab_task
from mjlab.tasks.velocity.rl import VelocityOnPolicyRunner
from .env_cfgs import (
Leju_kuavo4_flat_env_cfg,
Leju_kuavo4_rough_env_cfg,
)
from .rl_cfg import Leju_kuavo4_ppo_runner_cfg
# 注册Rough地形任务
register_mjlab_task(
task_id="Mjlab-Velocity-Rough-Leju-Kuavo4",
env_cfg=Leju_kuavo4_rough_env_cfg(),
play_env_cfg=Leju_kuavo4_rough_env_cfg(play=True),
rl_cfg=Leju_kuavo4_ppo_runner_cfg(),
runner_cls=VelocityOnPolicyRunner,
)
# 注册Flat地形任务
register_mjlab_task(
task_id="Mjlab-Velocity-Flat-Leju-Kuavo4",
env_cfg=Leju_kuavo4_flat_env_cfg(),
play_env_cfg=Leju_kuavo4_ppo_runner_cfg(play=True),
rl_cfg=Leju_kuavo4_ppo_runner_cfg(),
runner_cls=VelocityOnPolicyRunner,
)
5. Mimic/Tracking 策略配置
5.1 环境配置 (env_cfgs.py)
文件位置：mjlab/tasks/tracking/config/leju_kuavo4/env_cfgs.py
"""Leju Kuavo4 motion tracking environment configurations."""
from mjlab.asset_zoo.robots import (
KUAVO4_ACTION_SCALE,
get_kuavo4_robot_cfg,
)
from mjlab.asset_zoo.robots.leju_kuavo4.kuavo4_constants import FULL_COLLISION
from mjlab.envs import ManagerBasedRlEnvCfg
from mjlab.envs.mdp.actions import JointPositionActionCfg
from mjlab.managers.observation_manager import ObservationGroupCfg
from mjlab.sensor import ContactMatch, ContactSensorCfg
from mjlab.tasks.tracking.mdp import MotionCommandCfg
from mjlab.tasks.tracking.tracking_env_cfg import make_tracking_env_cfg
def Leju_kuavo4_flat_tracking_env_cfg(
has_state_estimation: bool = True,
play: bool = False,
) -> ManagerBasedRlEnvCfg:
"""Create Leju Kuavo4 flat terrain tracking configuration.
Args:
has_state_estimation: 是否包含状态估计（position observation）
play: 是否为play模式（启用自碰撞）
"""
cfg = make_tracking_env_cfg()
# Play模式使用FULL_COLLISION启用自碰撞
if play:
robot_cfg = get_kuavo4_robot_cfg()
robot_cfg.collisions = (FULL_COLLISION,)
cfg.scene.entities = {"robot": robot_cfg}
else:
cfg.scene.entities = {"robot": get_kuavo4_robot_cfg()}
# 自碰撞传感器
self_collision_cfg = ContactSensorCfg(
name="self_collision",
primary=ContactMatch(mode="subtree", pattern="base_link", entity="robot"),
secondary=ContactMatch(mode="subtree", pattern="base_link", entity="robot"),
fields=("found",),
reduce="none",
num_slots=1,
)
cfg.scene.sensors = (self_collision_cfg,)
# ========================================
# Action配置
# ========================================
joint_pos_action = cfg.actions["joint_pos"]
assert isinstance(joint_pos_action, JointPositionActionCfg)
joint_pos_action.scale = KUAVO4_ACTION_SCALE
# ========================================
# 运动命令配置（关键！）
# ========================================
motion_cmd = cfg.commands["motion"]
assert isinstance(motion_cmd, MotionCommandCfg)
motion_cmd.anchor_body_name = "base_link"
# Kuavo4有26个训练用DOF（无头部关节）
# 配置用于追踪的body名称
motion_cmd.body_names = (
"base_link", # 躯干
"leg_l1_link", # 左髋roll
"leg_l4_link", # 左膝
"leg_l6_link", # 左踝roll
"leg_r1_link", # 右髋roll
"leg_r4_link", # 右膝
"leg_r6_link", # 右踝roll
"zarm_l1_link", # 左肩pitch
"zarm_l4_link", # 左肘
"zarm_l7_link", # 左腕
"zarm_r1_link", # 右肩pitch
"zarm_r4_link", # 右肘
"zarm_r7_link", # 右腕
)
# 脚部摩擦事件
cfg.events["foot_friction"].params[
"asset_cfg"
].geom_names = r"^(left|right)_foot[1-7]_collision$"
# 质心随机化
cfg.events["base_com"].params["asset_cfg"].body_names = ("base_link",)
# 终止条件 - 末端执行器body名称
cfg.terminations["ee_body_pos"].params["body_names"] = (
"leg_l6_link", # 左踝
"leg_r6_link", # 右踝
"zarm_l7_link", # 左腕
"zarm_r7_link", # 右腕
)
# Viewer配置
cfg.viewer.body_name = "base_link"
# ========================================
# 传感器名称修正（关键！）
# ========================================
# Kuavo4使用BodyGyro而非imu_ang_vel
cfg.observations["policy"].terms["base_ang_vel"].params["sensor_name"] = "robot/BodyGyro"
cfg.observations["critic"].terms["base_ang_vel"].params["sensor_name"] = "robot/BodyGyro"
# base_lin_vel只在critic中使用
if "base_lin_vel" in cfg.observations["critic"].terms:
cfg.observations["critic"].terms["base_lin_vel"].params["sensor_name"] = "robot/BodyVel"
# ========================================
# 自碰撞nconmax限制（关键！）
# ========================================
# Kuavo4启用FULL_COLLISION后，接触点数量增加
cfg.sim.nconmax = 500
# ========================================
# 无状态估计模式
# ========================================
if not has_state_estimation:
new_policy_terms = {
k: v
for k, v in cfg.observations["policy"].terms.items()
if k not in ["motion_anchor_pos_b"]
}
cfg.observations["policy"] = ObservationGroupCfg(
terms=new_policy_terms,
concatenate_terms=True,
enable_corruption=True,
)
# 重新应用传感器名称修正
cfg.observations["policy"].terms["base_ang_vel"].params["sensor_name"] = "robot/BodyGyro"
# ========================================
# Play模式配置
# ========================================
if play:
cfg.episode_length_s = int(1e9) # 无限episode长度
cfg.observations["policy"].enable_corruption = False
cfg.events.pop("push_robot", None)
# 禁用RSI随机化
motion_cmd.pose_range = {}
motion_cmd.velocity_range = {}
motion_cmd.sampling_mode = "start"
return cfg
5.2 RL配置 (rl_cfgs.py)
"""Leju Kuavo4 tracking RL configuration."""
from mjlab.rl import (
RslRlOnPolicyRunnerCfg,
RslRlPpoActorCriticCfg,
RslRlPpoAlgorithmCfg,
)
def Leju_kuavo4_tracking_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg:
"""Create RL runner configuration for Leju Kuavo4 tracking task."""
return RslRlOnPolicyRunnerCfg(
policy=RslRlPpoActorCriticCfg(
init_noise_std=1.0,
actor_obs_normalization=True,
critic_obs_normalization=True,
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=0.005, # 比velocity稍低，因为动作多样性来自运动数据
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,
),
experiment_name="kuavo4_tracking",
save_interval=500,
num_steps_per_env=24,
max_iterations=30001, # Mimic通常需要更多iteration
)
5.3 任务注册 (init.py)
"""Tracking task registration for Leju Kuavo4."""
from mjlab.tasks.registry import register_mjlab_task
from mjlab.tasks.tracking.rl import MotionTrackingOnPolicyRunner
from .env_cfgs import Leju_kuavo4_flat_tracking_env_cfg
from .rl_cfgs import Leju_kuavo4_tracking_ppo_runner_cfg
# 标准mimic任务
register_mjlab_task(
task_id="Mjlab-Tracking-Flat-Leju-Kuavo4",
env_cfg=Leju_kuavo4_flat_tracking_env_cfg(),
play_env_cfg=Leju_kuavo4_flat_tracking_env_cfg(play=True),
rl_cfg=Leju_kuavo4_tracking_ppo_runner_cfg(),
runner_cls=MotionTrackingOnPolicyRunner,
)
# 无状态估计版本
register_mjlab_task(
task_id="Mjlab-Tracking-Flat-Leju-Kuavo4-No-State-Estimation",
env_cfg=Leju_kuavo4_flat_tracking_env_cfg(has_state_estimation=False),
play_env_cfg=Leju_kuavo4_flat_tracking_env_cfg(has_state_estimation=False, play=True),
rl_cfg=Leju_kuavo4_tracking_ppo_runner_cfg(),
runner_cls=MotionTrackingOnPolicyRunner,
)
6. 运动数据准备
6.1 CSV格式要求
Kuavo4的CSV运动数据格式：
# 格式: px, py, pz, qx, qy, qz, qw, joint1, joint2, ..., joint28
# 注意: 四元数是xyzw顺序
0.0, 0.0, 0.82, 0, 0, 0, 1, 0.0, 0.0, -0.1, 0.3, -0.15, 0.0, 0.0, ..., 0.0
...
- 前7列: 基座位置(px,py,pz)和四元数(qx,qy,qz,qw)
- 后28列: 所有28个关节（包含2个头部关节）
6.2 关节顺序
CSV中的关节顺序（28个）:
1-6: leg_l1-6_joint (左腿)
7-12: leg_r1-6_joint (右腿)
13-19: zarm_l1-7_joint (左臂)
20-26: zarm_r1-7_joint (右臂)
27-28: zhead_1,2_joint (头部) - 训练时跳过
6.3 csv_to_npz.py中的Kuavo4配置
# scripts/csv_to_npz.py
def main(
# ... 参数 ...
robot: str = "g1",
):
# ...
# Kuavo4配置
if robot == "kuavo4":
env_cfg = Leju_kuavo4_flat_tracking_env_cfg()
motion_dir = "kuavo4"
csv_dof_skip = 2 # 跳过2个头部关节 (28->26 DOF)
def get_joint_names(robot: str) -> list[str]:
"""Get joint names for the specified robot type."""
if robot == "kuavo4":
# Kuavo4有26个训练用DOF（无头部关节）
# CSV有28个关节，跳过最后2个头部关节
return [
# 腿部 (12个)
"leg_l1_joint", # 左髋roll
"leg_l2_joint", # 左髋yaw
"leg_l3_joint", # 左髋pitch
"leg_l4_joint", # 左膝
"leg_l5_joint", # 左踝pitch
"leg_l6_joint", # 左踝roll
"leg_r1_joint", # 右髋roll
"leg_r2_joint", # 右髋yaw
"leg_r3_joint", # 右髋pitch
"leg_r4_joint", # 右膝
"leg_r5_joint", # 右踝pitch
"leg_r6_joint", # 右踝roll
# 手臂 (14个)
"zarm_l1_joint", # 左臂pitch
"zarm_l2_joint", # 左臂roll
"zarm_l3_joint", # 左臂yaw
"zarm_l4_joint", # 左肘
"zarm_l5_joint", # 左手腕yaw
"zarm_l6_joint", # 左手腕roll
"zarm_l7_joint", # 左手腕pitch
"zarm_r1_joint", # 右臂pitch
"zarm_r2_joint", # 右臂roll
"zarm_r3_joint", # 右臂yaw
"zarm_r4_joint", # 右肘
"zarm_r5_joint", # 右手腕yaw
"zarm_r6_joint", # 右手腕roll
"zarm_r7_joint", # 右手腕pitch
# 跳过头部关节 (zhead_1_joint, zhead_2_joint)
]
# ...
6.4 转换命令
# 基本转换
python scripts/csv_to_npz.py \
--input-file /path/to/kuavo4_motion.csv \
--output-name kuavo4_mimic.npz \
--input-fps 30 \
--output-fps 50 \
--robot kuavo4
# 转换部分数据（例如第100-500行）
python scripts/csv_to_npz.py \
--input-file /path/to/kuavo4_motion.csv \
--output-name kuavo4_mimic.npz \
--input-fps 30 \
--output-fps 50 \
--robot kuavo4 \
--line-range 100 500
# 使用CPU（如果CUDA有问题）
python scripts/csv_to_npz.py \
--input-file /path/to/kuavo4_motion.csv \
--output-name kuavo4_mimic.npz \
--input-fps 30 \
--output-fps 50 \
--robot kuavo4 \
--device cpu
6.5 放置运动数据
正常转换完成，会自动保存在该路径下
# 创建目录
mkdir -p mjlab/motions/kuavo4/
# 移动转换后的NPZ文件
mv kuavo4_mimic.npz mjlab/motions/kuavo4/
7. 训练与验证
7.1 Velocity策略
训练命令
# Flat地形训练（推荐从flat开始）
python scripts/train.py \
Mjlab-Velocity-Flat-Leju-Kuavo4 \
--env.scene.num-envs=4096
# Rough地形训练
python scripts/train.py \
Mjlab-Velocity-Rough-Leju-Kuavo4 \
--env.scene.num-envs=4096
# 减少环境数量（如果GPU内存不足）
python scripts/train.py \
Mjlab-Velocity-Flat-Leju-Kuavo4 \
--env.scene.num-envs=2048
Play/验证命令
# 加载训练好的模型
python scripts/play.py \
Mjlab-Velocity-Flat-Leju-Kuavo4 \
--checkpoint_file=logs/rsl_rl/kuavo4_velocity/2026-02-01_20-48-02/model_2500.pt
乐聚kuavo机器人mjlab适配_哔哩哔哩_bilibili
7.2 Mimic/Tracking策略
训练命令
# mimic训练
python scripts/train.py \
Mjlab-Tracking-Flat-Leju-Kuavo4 \
--motion-file mjlab/motions/kuavo4/Kuavo_mjlab.npz \
--env.scene.num-envs=4096
Play/验证命令
# 加载训练好的模型
python scripts/play.py Mjlab-Tracking-Flat-Leju-Kuavo4 \
--motion-file mjlab/motions/kuavo4/Kuavo_mjlab.npz \
--checkpoint_file=logs/rsl_rl/kuavo4_tracking/2026-02-04_18-08-48/model_2000.pt
7.3 训练监控
# 启动TensorBoard
tensorboard --logdir=logs/
8. 常见问题与解决方案
8.1 Velocity任务问题
问题1: 自碰撞不工作
症状： 评测时机器人自穿模
解决方案：
# 在 env_cfgs.py 中检查
if play:
robot_cfg = get_kuavo4_robot_cfg()
robot_cfg.collisions = (FULL_COLLISION,) # 确保使用FULL_COLLISION
cfg.scene.entities = {"robot": robot_cfg}
同时检查XML文件中是否有contact配置：
<contact>
<exclude body1="base_link" body2="leg_l1_link" />
<!-- ... 28个排除配置 ... -->
</contact>
问题2: nconmax溢出
错误信息：
ValueError: nconmax must be >= 36, got 35
解决方案：
# 在 env_cfgs.py 中增加
cfg.sim.nconmax = 500
问题3: 传感器名称错误
错误信息：
KeyError: "Scene element 'robot/imu_ang_vel' not found"
解决方案：
# Kuavo4使用BodyGyro而非imu_ang_vel
cfg.observations["policy"].terms["base_ang_vel"].params["sensor_name"] = "robot/BodyGyro"
cfg.observations["critic"].terms["base_lin_vel"].params["sensor_name"] = "robot/BodyVel"
8.2 Mimic/Tracking任务问题
问题1: 运动数据维度不匹配
错误信息：
RuntimeError: The size of tensor a (26) must match the size of tensor b (28)
原因： NPZ文件关节数与机器人不一致
解决方案：
# 重新转换CSV，指定正确的robot参数
python scripts/csv_to_npz.py \
--input-file motion.csv \
--output-name kuavo4_mimic.npz \
--robot kuavo4 \ # 关键！
--csv-dof-skip 2
问题2: Body名称不匹配
错误信息：
KeyError: 'zarm_l7_link' not found
解决方案：
- 检查XML中的实际body名称：
grep 'body name=' mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml | grep zarm_l7 - 在
env_cfgs.py中修正：motion_cmd.body_names = ( # ... 使用实际的body名称 ... ) cfg.terminations["ee_body_pos"].params["body_names"] = ( # ... 使用实际的body名称 ... )
8.3 调试技巧
检查机器人配置
# test_kuavo4.py
from mjlab.entity.entity import Entity
from mjlab.asset_zoo.robots.leju_kuavo4 import get_kuavo4_robot_cfg
robot = Entity(get_kuavo4_robot_cfg())
print(f"关节数: {robot.data.num_joints}")
print(f"执行器数: {robot.data.num_actuators}")
print(f"关节名称: {robot.joint_names}")
print(f"Body名称: {robot.body_names}")
检查运动数据
# check_motion.py
import numpy as np
data = np.load("mjlab/motions/kuavo4/kuavo4_mimic.npz")
print(f"FPS: {data['fps']}")
print(f"帧数: {data['joint_pos'].shape[0]}")
print(f"关节数: {data['joint_pos'].shape[1]}")
print(f"持续时间: {data['joint_pos'].shape[0] / data['fps'][0]:.1f}秒")
查看XML信息
# 查看所有关节
grep 'joint name=' mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml
# 查看传感器
grep '<gyro>\|<accelerometer>\|<velocimeter>' mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml
# 查看body名称
grep 'body name=' mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml | head -20
8.4 训练问题
问题: 训练不稳定
症状： reward持续下降或episode_length很短
可能原因及解决方案：
-
初始姿态不合理
- 检查
HOME_KEYFRAME中的关节位置 - 特别是手臂roll关节的左右对称性
- 检查
-
奖励权重不合理
- 调整
cfg.rewards中的权重 - 降低惩罚项的权重
- 调整
-
学习率过高
- 降低
learning_rate从 1.0e-3 到 5.0e-4
- 降低
-
环境数量过少
- 增加
num_envs从 2048 到 4096 或更高
- 增加
问题: GPU内存不足
错误信息：
CUDA out of memory
解决方案：
# 减少环境数量
python scripts/train.py Mjlab-Velocity-Flat-Leju-Kuavo4 --env.scene.num-envs=2048
9. 配置总结
9.1 Kuavo4配置关键点
| 配置项 | 值 | 说明 |
|---|---|---|
| 训练DOF | 28 | 包含头部关节 |
| 硬件DOF | 28 | 包含头部关节 |
| 基座名称 | base_link |
躯干链接 |
| 传感器 | BodyGyro, BodyVel |
注意命名差异 |
| nconmax | 500 | 支持自碰撞 |
| 站立高度 | 0.82m | 初始姿态 |
9.2 Velocity vs Tracking配置差异
| 配置项 | Velocity | Tracking |
|---|---|---|
| 输入 | 速度命令 | 运动数据 |
| entropy_coef | 0.01 | 0.005 |
| max_iterations | 10001 | 30001 |
| save_interval | 100 | 500 |
| 传感器需求 | 基础传感器 | 需要运动数据 |
9.3 文件检查清单
- [ ]
kuavo4_constants.py- 执行器配置完整 - [ ]
biped_s100045.xml- contact配置存在 - [ ]
velocity/env_cfgs.py- 传感器名称正确 - [ ]
tracking/env_cfgs.py- body名称正确 - [ ]
csv_to_npz.py- kuavo4配置存在 - [ ] 运动数据NPZ文件存在
9.4 快速参考
# 常用命令速查
# 查看关节数
grep 'joint name=' mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml | wc -l
# 查看传感器
grep '<gyro>\|<velocimeter>' mjlab/asset_zoo/robots/leju_kuavo4/xml/biped_s100045.xml
# Velocity训练
python scripts/train.py Mjlab-Velocity-Flat-Leju-Kuavo4 --env.scene.num-envs=4096
# Velocity play
python scripts/play.py Mjlab-Velocity-Flat-Leju-Kuavo4 \
--checkpoint_file=logs/rsl_rl/kuavo4_velocity/2026-02-01_20-48-02/model_2500.pt
# 转换运动数据
python scripts/csv_to_npz.py \
--input-file ../GMR/motions/Kuavo_mjalb/csv/Kuavo_mjalb.csv \
--output-name Kuavo_mjlab.npz --input-fps 30 --output-fps 50 \
--robot kuavo4 --device cpu
# Tracking训练
python scripts/train.py Mjlab-Tracking-Flat-Leju-Kuavo4 --motion-file mjlab/motions/kuavo4/Kuavo_mjlab.npz --env.scene.num-envs=4096
# Tracking play
python scripts/play.py Mjlab-Tracking-Flat-Leju-Kuavo4 \
--motion-file mjlab/motions/kuavo4/Kuavo_mjlab.npz \
--checkpoint_file=logs/rsl_rl/kuavo4_tracking/2026-02-04_18-08-48/model_2000.pt
致谢
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