将一个新的机器人模型导入最新版isaacLab进行训练（以unitree H1_2为例）_isaaclab导入usdf

1、资产导入

• 在isaacgym中，机器人结构文件(.urdf)保存在resources目录下
• 在isaaclab的文件结构里有所不同，isaaclab使用USD格式文件，此时导入新机器人需要将urdf文件转换成USD文件。

1.1 文件准备

首先在宇树官方文档中拿到RL例程，把unitree_rl_gym文件夹放在根目录\\home\\username\\下

1.2 资产导入

需要调用scripts/tools/convert_urdf.py脚本进行转换，其中涉及到一些参数的设置：

参数名 描述 默认值 --merge-joints 布尔标志，设置为True时，合并由固定关节连接的连杆 False --fix-base 布尔标志，设置为True时，将机器人基座固定在导入位置 False --joint-stiffness 关节驱动的刚度，刚度值越大，关节越难变形 100.0 --joint-damping 关节驱动的阻尼，用于减少关节的振动和摆动 1.0 --joint-target-type 关节驱动的控制类型，可选值为\"position\"、“velocity\"或\"none” “position”

cd IsaacLab./isaaclab.sh -p scripts/tools/convert_urdf.py \\~/unitree_rl_gym/resources/robots/h1_2/h1_2.urdf \\source/isaaclab_assets/data/Robots/h1_2/h1_2.usd \\--merge-joints --joint-stiffness 0.0 \\--joint-damping 0.0 \\--joint-target-type none

导入到isaacsim中：

这个时候USD文件就生成了。

2、机器人属性配置

对标gym中的config，在IsaacLab中也要写一个对应的config。
在IsaacLab/source/isaaclab_assets/isaaclab_assets/robots/中找到了机器人们的配置文件，其中有一个文件为unitree.py，里面配置了Isaaclab收录的所有宇树机器人，但是H1_2恰好没在其中。

模仿unitree.py中关于H1的配置，再参考H1_2的关节，写一段config插在unitree.py中：
其中有几点需要注意：

• usd_path需要对应自己的usd文件的路径
• H1_2相比于H1而言，关节名称有些许变化（具体是名称后面多了“_joint”，以及ankle等部位多加了关节），正则表达式匹配需要在后面也加个 $\text{.*}$

H1_2_CFG = ArticulationCfg( spawn=sim_utils.UsdFileCfg( usd_path=f\"/home/swanchan/IsaacLab/source/isaaclab_assets/data/Robots/h1_2/h1_2.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=4 ), ), init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.0, 1.05), joint_pos={ \".*_hip_yaw.*\": 0.0, \".*_hip_roll.*\": 0.0, \".*_hip_pitch.*\": -0.16, # -9.17 degrees \".*_knee.*\": 0.36, # 20.63 degrees \".*_ankle_pitch.*\": -0.2, # -11.46 degrees \".*_ankle_roll.*\": 0.0, \"torso.*\": 0.0, \".*_shoulder_pitch.*\": 0.4, # 22.92 degrees \".*_shoulder_roll.*\": 0.0, \".*_shoulder_yaw.*\": 0.0, \".*_elbow_pitch.*\": 0.3, # 17.19 degrees }, joint_vel={\".*\": 0.0}, ), soft_joint_pos_limit_factor=0.9, actuators={ \"legs\": ImplicitActuatorCfg( joint_names_expr=[\".*_hip_yaw.*\", \".*_hip_roll.*\", \".*_hip_pitch.*\", \".*_knee.*\", \"torso.*\"], effort_limit=300, velocity_limit=100.0, stiffness={ \".*_hip_yaw.*\": 200.0, \".*_hip_roll.*\": 200.0, \".*_hip_pitch.*\": 200.0, \".*_knee.*\": 300.0, \"torso.*\": 200.0, }, damping={ \".*_hip_yaw.*\": 2.5, \".*_hip_roll.*\": 2.5, \".*_hip_pitch.*\": 2.5, \".*_knee.*\": 4.0, \"torso.*\": 5.0, }, ), \"feet\": ImplicitActuatorCfg( joint_names_expr=[\".*_ankle_pitch.*\", \".*_ankle_roll.*\"], effort_limit=100, velocity_limit=100.0, stiffness={ \".*_ankle_pitch.*\": 40.0, \".*_ankle_roll.*\": 40.0, }, damping={ \".*_ankle_pitch.*\": 2.0, \".*_ankle_roll.*\": 2.0, }, ), \"arms\": ImplicitActuatorCfg( joint_names_expr=[\".*_shoulder_pitch.*\", \".*_shoulder_roll.*\", \".*_shoulder_yaw.*\", \".*_elbow_pitch.*\"], effort_limit=300, velocity_limit=100.0, stiffness={ \".*_shoulder_pitch.*\": 40.0, \".*_shoulder_roll.*\": 40.0, \".*_shoulder_yaw.*\": 40.0, \".*_elbow_pitch.*\": 40.0, }, damping={ \".*_shoulder_pitch.*\": 10.0, \".*_shoulder_roll.*\": 10.0, \".*_shoulder_yaw.*\": 10.0, \".*_elbow_pitch.*\": 10.0, }, ), },)\"\"\"Configuration for the Unitree H1_2 Humanoid robot.\"\"\"H1_2_MINIMAL_CFG = H1_2_CFG.copy()H1_2_MINIMAL_CFG.spawn.usd_path = f\"{ISAACLAB_NUCLEUS_DIR}/Robots/Unitree/H1_2/h1_2_minimal.usd\"

3、强化学习任务环境配置

在/IsaacLab/source/isaaclab_tasks/isaaclab_tasks/manager_based/locomotion/velocity/config/中可以看到宇树的机器人训练环境

直接把h1文件夹复制为h1_2，然后对里面的文件进行一定的更改。
具体是把所有h1替换为h1_2，所有H1替换为H1_2，再改一下rough_env_cfg.py里面的关节

__init__.py

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.# All rights reserved.## SPDX-License-Identifier: BSD-3-Clauseimport gymnasium as gymfrom . import agents### Register Gym environments.##gym.register( id=\"Isaac-Velocity-Rough-H1_2-v0\", entry_point=\"isaaclab.envs:ManagerBasedRLEnv\", disable_env_checker=True, kwargs={ \"env_cfg_entry_point\": f\"{__name__}.rough_env_cfg:H1_2RoughEnvCfg\", \"rsl_rl_cfg_entry_point\": f\"{agents.__name__}.rsl_rl_ppo_cfg:H1_2RoughPPORunnerCfg\", \"skrl_cfg_entry_point\": f\"{agents.__name__}:skrl_rough_ppo_cfg.yaml\", },)gym.register( id=\"Isaac-Velocity-Rough-H1_2-Play-v0\", entry_point=\"isaaclab.envs:ManagerBasedRLEnv\", disable_env_checker=True, kwargs={ \"env_cfg_entry_point\": f\"{__name__}.rough_env_cfg:H1_2RoughEnvCfg_PLAY\", \"rsl_rl_cfg_entry_point\": f\"{agents.__name__}.rsl_rl_ppo_cfg:H1_2RoughPPORunnerCfg\", \"skrl_cfg_entry_point\": f\"{agents.__name__}:skrl_rough_ppo_cfg.yaml\", },)gym.register( id=\"Isaac-Velocity-Flat-H1_2-v0\", entry_point=\"isaaclab.envs:ManagerBasedRLEnv\", disable_env_checker=True, kwargs={ \"env_cfg_entry_point\": f\"{__name__}.flat_env_cfg:H1_2FlatEnvCfg\", \"rsl_rl_cfg_entry_point\": f\"{agents.__name__}.rsl_rl_ppo_cfg:H1_2FlatPPORunnerCfg\", \"skrl_cfg_entry_point\": f\"{agents.__name__}:skrl_flat_ppo_cfg.yaml\", },)gym.register( id=\"Isaac-Velocity-Flat-H1_2-Play-v0\", entry_point=\"isaaclab.envs:ManagerBasedRLEnv\", disable_env_checker=True, kwargs={ \"env_cfg_entry_point\": f\"{__name__}.flat_env_cfg:H1_2FlatEnvCfg_PLAY\", \"rsl_rl_cfg_entry_point\": f\"{agents.__name__}.rsl_rl_ppo_cfg:H1_2FlatPPORunnerCfg\", \"skrl_cfg_entry_point\": f\"{agents.__name__}:skrl_flat_ppo_cfg.yaml\", },)

flat_env_cfg.py

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.# All rights reserved.## SPDX-License-Identifier: BSD-3-Clausefrom isaaclab.utils import configclassfrom .rough_env_cfg import H1_2RoughEnvCfg@configclassclass H1_2FlatEnvCfg(H1_2RoughEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # 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 # no terrain curriculum self.curriculum.terrain_levels = None self.rewards.feet_air_time.weight = 1.0 self.rewards.feet_air_time.params[\"threshold\"] = 0.6class H1_2FlatEnvCfg_PLAY(H1_2FlatEnvCfg): def __post_init__(self) -> None: # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 # disable randomization for play self.observations.policy.enable_corruption = False # remove random pushing self.events.base_external_force_torque = None self.events.push_robot = None

rough_env_cfg.py

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.# All rights reserved.## SPDX-License-Identifier: BSD-3-Clausefrom isaaclab.managers import RewardTermCfg as RewTermfrom isaaclab.managers import SceneEntityCfgfrom isaaclab.utils import configclassimport isaaclab_tasks.manager_based.locomotion.velocity.mdp as mdpfrom isaaclab_tasks.manager_based.locomotion.velocity.velocity_env_cfg import LocomotionVelocityRoughEnvCfg, RewardsCfg### Pre-defined configs##from isaaclab_assets import H1_2_CFG@configclassclass H1_2Rewards(RewardsCfg): \"\"\"Reward terms for the MDP.\"\"\" termination_penalty = RewTerm(func=mdp.is_terminated, weight=-200.0) lin_vel_z_l2 = None track_lin_vel_xy_exp = RewTerm( func=mdp.track_lin_vel_xy_yaw_frame_exp, weight=1.0, params={\"command_name\": \"base_velocity\", \"std\": 0.5}, ) track_ang_vel_z_exp = RewTerm( func=mdp.track_ang_vel_z_world_exp, weight=1.0, params={\"command_name\": \"base_velocity\", \"std\": 0.5} ) feet_air_time = RewTerm( func=mdp.feet_air_time_positive_biped, weight=0.25, params={ \"command_name\": \"base_velocity\", \"sensor_cfg\": SceneEntityCfg(\"contact_forces\", body_names=\".*ankle.*\"), \"threshold\": 0.4, }, ) feet_slide = RewTerm( func=mdp.feet_slide, weight=-0.25, params={ \"sensor_cfg\": SceneEntityCfg(\"contact_forces\", body_names=\".*ankle.*\"), \"asset_cfg\": SceneEntityCfg(\"robot\", body_names=\".*ankle.*\"), }, ) # Penalize ankle joint limits dof_pos_limits = RewTerm( func=mdp.joint_pos_limits, weight=-1.0, params={\"asset_cfg\": SceneEntityCfg(\"robot\", joint_names=\".*_ankle.*\")} ) # Penalize deviation from default of the joints that are not essential for locomotion joint_deviation_hip = RewTerm( func=mdp.joint_deviation_l1, weight=-0.2, params={\"asset_cfg\": SceneEntityCfg(\"robot\", joint_names=[\".*_hip_yaw.*\", \".*_hip_roll.*\"])}, ) joint_deviation_arms = RewTerm( func=mdp.joint_deviation_l1, weight=-0.2, params={\"asset_cfg\": SceneEntityCfg(\"robot\", joint_names=[\".*_shoulder_.*\", \".*_elbow.*\"])}, ) joint_deviation_torso = RewTerm( func=mdp.joint_deviation_l1, weight=-0.1, params={\"asset_cfg\": SceneEntityCfg(\"robot\", joint_names=\"torso.*\")} )@configclassclass H1_2RoughEnvCfg(LocomotionVelocityRoughEnvCfg): rewards: H1_2Rewards = H1_2Rewards() def __post_init__(self): # post init of parent super().__post_init__() # Scene self.scene.robot = H1_2_CFG.replace(prim_path=\"{ENV_REGEX_NS}/Robot\") # type: ignore if self.scene.height_scanner: self.scene.height_scanner.prim_path = \"{ENV_REGEX_NS}/Robot/torso_link\" # Randomization self.events.push_robot = None self.events.add_base_mass = None self.events.reset_robot_joints.params[\"position_range\"] = (1.0, 1.0) self.events.base_external_force_torque.params[\"asset_cfg\"].body_names = [\".*torso_link\"] self.events.reset_base.params = { \"pose_range\": {\"x\": (-0.5, 0.5), \"y\": (-0.5, 0.5), \"yaw\": (-3.14, 3.14)}, \"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), }, } # Terminations self.terminations.base_contact.params[\"sensor_cfg\"].body_names = [\".*torso_link\"] # Rewards self.rewards.undesired_contacts = None self.rewards.flat_orientation_l2.weight = -1.0 self.rewards.dof_torques_l2.weight = 0.0 self.rewards.action_rate_l2.weight = -0.005 self.rewards.dof_acc_l2.weight = -1.25e-7 # Commands self.commands.base_velocity.ranges.lin_vel_x = (0.0, 1.0) self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) # terminations self.terminations.base_contact.params[\"sensor_cfg\"].body_names = \".*torso_link\"@configclassclass H1_2RoughEnvCfg_PLAY(H1_2RoughEnvCfg): def __post_init__(self): # post init of parent super().__post_init__() # make a smaller scene for play self.scene.num_envs = 50 self.scene.env_spacing = 2.5 self.episode_length_s = 40.0 # spawn the robot randomly in the grid (instead of their terrain levels) self.scene.terrain.max_init_terrain_level = None # reduce the number of terrains to save memory if self.scene.terrain.terrain_generator is not None: self.scene.terrain.terrain_generator.num_rows = 5 self.scene.terrain.terrain_generator.num_cols = 5 self.scene.terrain.terrain_generator.curriculum = False self.commands.base_velocity.ranges.lin_vel_x = (1.0, 1.0) self.commands.base_velocity.ranges.lin_vel_y = (0.0, 0.0) self.commands.base_velocity.ranges.ang_vel_z = (-1.0, 1.0) self.commands.base_velocity.ranges.heading = (0.0, 0.0) # disable randomization for play self.observations.policy.enable_corruption = False # remove random pushing self.events.base_external_force_torque = None self.events.push_robot = None

rsl_rl_ppo_cfg.py

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.# All rights reserved.## SPDX-License-Identifier: BSD-3-Clausefrom isaaclab.utils import configclassfrom isaaclab_rl.rsl_rl import RslRlOnPolicyRunnerCfg, RslRlPpoActorCriticCfg, RslRlPpoAlgorithmCfg@configclassclass H1_2RoughPPORunnerCfg(RslRlOnPolicyRunnerCfg): num_steps_per_env = 24 max_iterations = 3000 save_interval = 50 experiment_name = \"H1_2_rough\" empirical_normalization = False policy = RslRlPpoActorCriticCfg( init_noise_std=1.0, 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, )@configclassclass H1_2FlatPPORunnerCfg(H1_2RoughPPORunnerCfg): def __post_init__(self): super().__post_init__() self.max_iterations = 1000 self.experiment_name = \"H1_2_flat\" self.policy.actor_hidden_dims = [128, 128, 128] self.policy.critic_hidden_dims = [128, 128, 128]

最后，在IsaacLab目录下执行训练脚本，就可以开始训练啦

./isaaclab.sh -p scripts/reinforcement_learning/rsl_rl/train.py --task Isaac-Velocity-Rough-H1_2-v0 --headless