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# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Imitation learning tasks."""
import gymnasium as gym # noqa: F401

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# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import gymnasium as gym
# from . import agents
from .agents import *
##
# Register Gym environments.
##
##
# MindRobot Left Arm IK-Rel (keyboard teleop)
##
# gym.register(
# id="Isaac-MindRobot-LeftArm-IK-Rel-v0",
# entry_point="isaaclab.envs:ManagerBasedRLEnv",
# kwargs={
# "env_cfg_entry_point": (
# "isaaclab_tasks.manager_based.manipulation.mindrobot."
# "mindrobot_left_arm_ik_env_cfg:MindRobotLeftArmIKRelEnvCfg"
# ),
# "robomimic_bc_cfg_entry_point": "isaaclab_tasks.manager_based.manipulation.mindrobot.agents:robomimic/bc_rnn_low_dim.json",
# },
# disable_env_checker=True,
# )
# ...existing code...
gym.register(
id="Isaac-MindRobot-LeftArm-IK-Rel-v0",
entry_point="isaaclab.envs:ManagerBasedRLEnv",
kwargs={
"env_cfg_entry_point": f"{__name__}.mindrobot_left_arm_ik_env_cfg:MindRobotLeftArmIKEnvCfg",
"robomimic_bc_cfg_entry_point": f"{__name__}.agents:robomimic/bc_rnn_low_dim.json",
},
disable_env_checker=True,
)
# ...existing code...

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# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

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{
"algo_name": "bc",
"experiment": {
"name": "bc_rnn_image_franka_stack",
"validate": false,
"logging": {
"terminal_output_to_txt": true,
"log_tb": true
},
"save": {
"enabled": true,
"every_n_seconds": null,
"every_n_epochs": 20,
"epochs": [],
"on_best_validation": false,
"on_best_rollout_return": false,
"on_best_rollout_success_rate": true
},
"epoch_every_n_steps": 500,
"env": null,
"additional_envs": null,
"render": false,
"render_video": false,
"rollout": {
"enabled": false
}
},
"train": {
"data": null,
"num_data_workers": 4,
"hdf5_cache_mode": "low_dim",
"hdf5_use_swmr": true,
"hdf5_load_next_obs": false,
"hdf5_normalize_obs": false,
"hdf5_filter_key": null,
"hdf5_validation_filter_key": null,
"seq_length": 10,
"pad_seq_length": true,
"frame_stack": 1,
"pad_frame_stack": true,
"dataset_keys": [
"actions",
"rewards",
"dones"
],
"goal_mode": null,
"cuda": true,
"batch_size": 16,
"num_epochs": 600,
"seed": 101
},
"algo": {
"optim_params": {
"policy": {
"optimizer_type": "adam",
"learning_rate": {
"initial": 0.0001,
"decay_factor": 0.1,
"epoch_schedule": [],
"scheduler_type": "multistep"
},
"regularization": {
"L2": 0.0
}
}
},
"loss": {
"l2_weight": 1.0,
"l1_weight": 0.0,
"cos_weight": 0.0
},
"actor_layer_dims": [],
"gaussian": {
"enabled": false,
"fixed_std": false,
"init_std": 0.1,
"min_std": 0.01,
"std_activation": "softplus",
"low_noise_eval": true
},
"gmm": {
"enabled": true,
"num_modes": 5,
"min_std": 0.0001,
"std_activation": "softplus",
"low_noise_eval": true
},
"vae": {
"enabled": false,
"latent_dim": 14,
"latent_clip": null,
"kl_weight": 1.0,
"decoder": {
"is_conditioned": true,
"reconstruction_sum_across_elements": false
},
"prior": {
"learn": false,
"is_conditioned": false,
"use_gmm": false,
"gmm_num_modes": 10,
"gmm_learn_weights": false,
"use_categorical": false,
"categorical_dim": 10,
"categorical_gumbel_softmax_hard": false,
"categorical_init_temp": 1.0,
"categorical_temp_anneal_step": 0.001,
"categorical_min_temp": 0.3
},
"encoder_layer_dims": [
300,
400
],
"decoder_layer_dims": [
300,
400
],
"prior_layer_dims": [
300,
400
]
},
"rnn": {
"enabled": true,
"horizon": 10,
"hidden_dim": 1000,
"rnn_type": "LSTM",
"num_layers": 2,
"open_loop": false,
"kwargs": {
"bidirectional": false
}
},
"transformer": {
"enabled": false,
"context_length": 10,
"embed_dim": 512,
"num_layers": 6,
"num_heads": 8,
"emb_dropout": 0.1,
"attn_dropout": 0.1,
"block_output_dropout": 0.1,
"sinusoidal_embedding": false,
"activation": "gelu",
"supervise_all_steps": false,
"nn_parameter_for_timesteps": true
}
},
"observation": {
"modalities": {
"obs": {
"low_dim": [
"eef_pos",
"eef_quat",
"gripper_pos"
],
"rgb": [
"table_cam",
"wrist_cam"
],
"depth": [],
"scan": []
},
"goal": {
"low_dim": [],
"rgb": [],
"depth": [],
"scan": []
}
},
"encoder": {
"low_dim": {
"core_class": null,
"core_kwargs": {},
"obs_randomizer_class": null,
"obs_randomizer_kwargs": {}
},
"rgb": {
"core_class": "VisualCore",
"core_kwargs": {
"feature_dimension": 64,
"flatten": true,
"backbone_class": "ResNet18Conv",
"backbone_kwargs": {
"pretrained": false,
"input_coord_conv": false
},
"pool_class": "SpatialSoftmax",
"pool_kwargs": {
"num_kp": 32,
"learnable_temperature": false,
"temperature": 1.0,
"noise_std": 0.0,
"output_variance": false
}
},
"obs_randomizer_class": "CropRandomizer",
"obs_randomizer_kwargs": {
"crop_height": 181,
"crop_width": 181,
"num_crops": 1,
"pos_enc": false
}
},
"depth": {
"core_class": "VisualCore",
"core_kwargs": {},
"obs_randomizer_class": null,
"obs_randomizer_kwargs": {}
},
"scan": {
"core_class": "ScanCore",
"core_kwargs": {},
"obs_randomizer_class": null,
"obs_randomizer_kwargs": {}
}
}
}
}

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{
"algo_name": "bc",
"experiment": {
"name": "bc_rnn_image_franka_stack_cosmos",
"validate": false,
"logging": {
"terminal_output_to_txt": true,
"log_tb": true
},
"save": {
"enabled": true,
"every_n_seconds": null,
"every_n_epochs": 20,
"epochs": [],
"on_best_validation": false,
"on_best_rollout_return": false,
"on_best_rollout_success_rate": true
},
"epoch_every_n_steps": 500,
"env": null,
"additional_envs": null,
"render": false,
"render_video": false,
"rollout": {
"enabled": false
}
},
"train": {
"data": null,
"num_data_workers": 4,
"hdf5_cache_mode": "low_dim",
"hdf5_use_swmr": true,
"hdf5_load_next_obs": false,
"hdf5_normalize_obs": false,
"hdf5_filter_key": null,
"hdf5_validation_filter_key": null,
"seq_length": 10,
"pad_seq_length": true,
"frame_stack": 1,
"pad_frame_stack": true,
"dataset_keys": [
"actions",
"rewards",
"dones"
],
"goal_mode": null,
"cuda": true,
"batch_size": 16,
"num_epochs": 600,
"seed": 101
},
"algo": {
"optim_params": {
"policy": {
"optimizer_type": "adam",
"learning_rate": {
"initial": 0.0001,
"decay_factor": 0.1,
"epoch_schedule": [],
"scheduler_type": "multistep"
},
"regularization": {
"L2": 0.0
}
}
},
"loss": {
"l2_weight": 1.0,
"l1_weight": 0.0,
"cos_weight": 0.0
},
"actor_layer_dims": [],
"gaussian": {
"enabled": false,
"fixed_std": false,
"init_std": 0.1,
"min_std": 0.01,
"std_activation": "softplus",
"low_noise_eval": true
},
"gmm": {
"enabled": true,
"num_modes": 5,
"min_std": 0.0001,
"std_activation": "softplus",
"low_noise_eval": true
},
"vae": {
"enabled": false,
"latent_dim": 14,
"latent_clip": null,
"kl_weight": 1.0,
"decoder": {
"is_conditioned": true,
"reconstruction_sum_across_elements": false
},
"prior": {
"learn": false,
"is_conditioned": false,
"use_gmm": false,
"gmm_num_modes": 10,
"gmm_learn_weights": false,
"use_categorical": false,
"categorical_dim": 10,
"categorical_gumbel_softmax_hard": false,
"categorical_init_temp": 1.0,
"categorical_temp_anneal_step": 0.001,
"categorical_min_temp": 0.3
},
"encoder_layer_dims": [
300,
400
],
"decoder_layer_dims": [
300,
400
],
"prior_layer_dims": [
300,
400
]
},
"rnn": {
"enabled": true,
"horizon": 10,
"hidden_dim": 1000,
"rnn_type": "LSTM",
"num_layers": 2,
"open_loop": false,
"kwargs": {
"bidirectional": false
}
},
"transformer": {
"enabled": false,
"context_length": 10,
"embed_dim": 512,
"num_layers": 6,
"num_heads": 8,
"emb_dropout": 0.1,
"attn_dropout": 0.1,
"block_output_dropout": 0.1,
"sinusoidal_embedding": false,
"activation": "gelu",
"supervise_all_steps": false,
"nn_parameter_for_timesteps": true
}
},
"observation": {
"modalities": {
"obs": {
"low_dim": [
"eef_pos",
"eef_quat",
"gripper_pos"
],
"rgb": [
"table_cam"
],
"depth": [],
"scan": []
},
"goal": {
"low_dim": [],
"rgb": [],
"depth": [],
"scan": []
}
},
"encoder": {
"low_dim": {
"core_class": null,
"core_kwargs": {},
"obs_randomizer_class": null,
"obs_randomizer_kwargs": {}
},
"rgb": {
"core_class": "VisualCore",
"core_kwargs": {
"feature_dimension": 64,
"flatten": true,
"backbone_class": "ResNet18Conv",
"backbone_kwargs": {
"pretrained": false,
"input_coord_conv": false
},
"pool_class": "SpatialSoftmax",
"pool_kwargs": {
"num_kp": 32,
"learnable_temperature": false,
"temperature": 1.0,
"noise_std": 0.0,
"output_variance": false
}
},
"obs_randomizer_class": "CropRandomizer",
"obs_randomizer_kwargs": {
"crop_height": 180,
"crop_width": 180,
"num_crops": 1,
"pos_enc": false
}
},
"depth": {
"core_class": "VisualCore",
"core_kwargs": {},
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"obs_randomizer_kwargs": {}
},
"scan": {
"core_class": "ScanCore",
"core_kwargs": {},
"obs_randomizer_class": null,
"obs_randomizer_kwargs": {}
}
}
}
}

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{
"algo_name": "bc",
"experiment": {
"name": "bc_rnn_low_dim_mindbot_stack",
"validate": false,
"logging": {
"terminal_output_to_txt": true,
"log_tb": true
},
"save": {
"enabled": true,
"every_n_seconds": null,
"every_n_epochs": 100,
"epochs": [],
"on_best_validation": false,
"on_best_rollout_return": false,
"on_best_rollout_success_rate": true
},
"epoch_every_n_steps": 100,
"env": null,
"additional_envs": null,
"render": false,
"render_video": false,
"rollout": {
"enabled": false
}
},
"train": {
"data": null,
"num_data_workers": 4,
"hdf5_cache_mode": "all",
"hdf5_use_swmr": true,
"hdf5_normalize_obs": false,
"hdf5_filter_key": null,
"hdf5_validation_filter_key": null,
"seq_length": 10,
"dataset_keys": [
"actions"
],
"goal_mode": null,
"cuda": true,
"batch_size": 100,
"num_epochs": 2000,
"seed": 101
},
"algo": {
"optim_params": {
"policy": {
"optimizer_type": "adam",
"learning_rate": {
"initial": 0.001,
"decay_factor": 0.1,
"epoch_schedule": [],
"scheduler_type": "multistep"
},
"regularization": {
"L2": 0.0
}
}
},
"loss": {
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"l1_weight": 0.0,
"cos_weight": 0.0
},
"actor_layer_dims": [],
"gmm": {
"enabled": true,
"num_modes": 5,
"min_std": 0.0001,
"std_activation": "softplus",
"low_noise_eval": true
},
"rnn": {
"enabled": true,
"horizon": 10,
"hidden_dim": 400,
"rnn_type": "LSTM",
"num_layers": 2,
"open_loop": false,
"kwargs": {
"bidirectional": false
}
}
},
"observation": {
"modalities": {
"obs": {
"low_dim": [
"eef_pos",
"eef_quat",
"gripper_pos",
"object"
],
"rgb": [],
"depth": [],
"scan": []
}
}
}
}

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# Copyright (c) 2024, MindRobot Project
# SPDX-License-Identifier: BSD-3-Clause
"""Configuration for MindRobot dual-arm robot."""
import isaaclab.sim as sim_utils
from isaaclab.actuators import ImplicitActuatorCfg
from isaaclab.assets import ArticulationCfg
##
# ✅ 使用你的本地 USD 绝对路径
##
MINDBOT_CFG = ArticulationCfg(
spawn=sim_utils.UsdFileCfg(
# =====================================================
# ✅ 你的本地 USD 文件路径
# =====================================================
usd_path="/home/tangger/DataDisk/maic_usd_assets/robots/mindrobot-1105-lab/mindrobot_cd2.usd",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
max_depenetration_velocity=1.0,
linear_damping=0.5,
angular_damping=0.5,
),
articulation_props=sim_utils.ArticulationRootPropertiesCfg(
fix_root_link=True,
enabled_self_collisions=False,
solver_position_iteration_count=16,
solver_velocity_iteration_count=8,
stabilization_threshold=1e-6,
),
collision_props=sim_utils.CollisionPropertiesCfg(
collision_enabled=True,
contact_offset=0.0005,
rest_offset=0,
),
),
init_state=ArticulationCfg.InitialStateCfg(
joint_pos={
# ====== 左臂关节(主控臂)======
"l_joint1": 2.3562, # 135°
"l_joint2": -1.2217, # -70°
"l_joint3": -1.5708, # -90°
"l_joint4": 1.5708, # 90°
"l_joint5": 1.5708, # 90°
"l_joint6": -1.2217, # -70°
# ====== 右臂关节(静止)======
"r_joint1": 0.0,
"r_joint2": 0.0,
"r_joint3": 0.0,
"r_joint4": 0.0,
"r_joint5": 0.0,
"r_joint6": 0.0,
# ====== 左手夹爪 ======
"left_hand_joint_left": 0.0, # 0.0=打开, -0.03=闭合
"left_hand_joint_right": 0.0, # 0.0=打开, 0.03=闭合
# ====== 右手夹爪 ======
"right_hand_joint_left": 0.0,
"right_hand_joint_right": 0.0,
# ====== 躯干 ======
"PrismaticJoint": 0.26,
# ====== 头部 ======
"head_revoluteJoint": 0.0,
},
pos=(0, 0, 0.05),
),
actuators={
"left_arm": ImplicitActuatorCfg(
joint_names_expr=["l_joint[1-6]"],
effort_limit_sim=100.0,
velocity_limit_sim=100.0,
# ✅ IK 控制需要高刚度
stiffness=10000.0,
damping=1000.0,
),
"right_arm": ImplicitActuatorCfg(
joint_names_expr=["r_joint[1-6]"],
effort_limit_sim=100.0,
velocity_limit_sim=100.0,
stiffness=10000.0,
damping=1000.0,
),
"head": ImplicitActuatorCfg(
joint_names_expr=["head_revoluteJoint"],
stiffness=10000.0,
damping=1000.0,
),
"trunk": ImplicitActuatorCfg(
joint_names_expr=["PrismaticJoint"],
stiffness=40000.0,
damping=4000.0,
effort_limit_sim=10000.0,
velocity_limit_sim=0.2,
),
"wheels": ImplicitActuatorCfg(
joint_names_expr=[".*_revolute_Joint"],
stiffness=0.0,
damping=100.0,
effort_limit_sim=200.0,
velocity_limit_sim=50.0,
),
# ✅ 夹爪:高刚度确保精准控制
"grippers": ImplicitActuatorCfg(
joint_names_expr=[".*_hand_joint.*"],
stiffness=10000.0,
damping=1000.0,
effort_limit_sim=50.0,
velocity_limit_sim=50.0,
),
},
)
##
# ✅ 专用于 IK 控制的高 PD 版本(可选但推荐)
##
MINDBOT_HIGH_PD_CFG = MINDBOT_CFG.copy()
MINDBOT_HIGH_PD_CFG.actuators["left_arm"].stiffness = 40000.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm"].damping = 4000.0

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# Copyright (c) 2024, MindRobot Project
# SPDX-License-Identifier: BSD-3-Clause
"""MindRobot left arm IK-Rel environment config for teleoperation."""
from __future__ import annotations
from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
import isaaclab.sim as sim_utils
from isaaclab.assets import ArticulationCfg, AssetBaseCfg, RigidObjectCfg
from isaaclab.controllers.differential_ik_cfg import DifferentialIKControllerCfg
from isaaclab.devices import DevicesCfg
from isaaclab.devices.keyboard import Se3KeyboardCfg
from isaaclab.devices.spacemouse import Se3SpaceMouseCfg
from isaaclab.devices.gamepad import Se3GamepadCfg
from isaaclab.envs import ManagerBasedRLEnvCfg
from isaaclab.envs.mdp.actions.actions_cfg import (
BinaryJointPositionActionCfg,
DifferentialInverseKinematicsActionCfg,
)
from isaaclab.managers import ObservationGroupCfg as ObsGroup
from isaaclab.managers import ObservationTermCfg as ObsTerm
from isaaclab.managers import TerminationTermCfg as DoneTerm
from isaaclab.scene import InteractiveSceneCfg
from isaaclab.sensors.frame_transformer.frame_transformer_cfg import FrameTransformerCfg
from isaaclab.sensors.frame_transformer.frame_transformer_cfg import OffsetCfg
from isaaclab.sim.schemas.schemas_cfg import RigidBodyPropertiesCfg
from isaaclab.sim.spawners.from_files.from_files_cfg import GroundPlaneCfg
from isaaclab.utils import configclass
from isaaclab.markers.config import FRAME_MARKER_CFG
from isaaclab.devices.openxr import XrCfg
# 导入基础配置和MDP函数
from isaaclab_tasks.manager_based.manipulation.stack import mdp
# 导入 MindRobot 资产
from .mindrobot_cfg import MINDBOT_HIGH_PD_CFG
# 在文件开头添加
import isaaclab.utils.assets as assets_utils
# # 然后在 scene 配置中使用
# spawn=sim_utils.UsdFileCfg(
# usd_path=f"{assets_utils.ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"
# ),
# =====================================================================
# Scene Configuration
# =====================================================================
@configclass
class MindRobotTeleopSceneCfg(InteractiveSceneCfg):
"""Minimal scene for MindRobot teleoperation: robot + table + optional cube."""
# Ground plane
plane = AssetBaseCfg(
prim_path="/World/GroundPlane",
init_state=AssetBaseCfg.InitialStateCfg(pos=[0, 0, -1.05]),
spawn=GroundPlaneCfg(),
)
# # Table
# table = AssetBaseCfg(
# prim_path="{ENV_REGEX_NS}/Table",
# init_state=AssetBaseCfg.InitialStateCfg(pos=[0.5, 0, 0], rot=[0.707, 0, 0, 0.707]),
# spawn=sim_utils.UsdFileCfg(
# usd_path=f"{assets_utils.ISAAC_NUCLEUS_DIR}/Props/Mounts/SeattleLabTable/table_instanceable.usd"
# ),
# )
# Optional: Single cube for testing (can be removed if not needed)
cube = RigidObjectCfg(
prim_path="{ENV_REGEX_NS}/Cube",
spawn=sim_utils.CuboidCfg(
size=(0.04, 0.04, 0.04),
rigid_props=RigidBodyPropertiesCfg(),
mass_props=sim_utils.MassPropertiesCfg(mass=0.1),
collision_props=sim_utils.CollisionPropertiesCfg(),
visual_material=sim_utils.PreviewSurfaceCfg(
diffuse_color=(1.0, 0.0, 0.0)
),
),
init_state=RigidObjectCfg.InitialStateCfg(pos=(0.5, 0.0, 0.45)),
)
# MindRobot
robot: ArticulationCfg = MINDBOT_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
# Lighting
light = AssetBaseCfg(
prim_path="/World/light",
spawn=sim_utils.DomeLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
)
# End-effector frame transformer
ee_frame: FrameTransformerCfg = None # Will be set in __post_init__
# =====================================================================
# Actions Configuration
# =====================================================================
@configclass
class MindRobotTeleopActionsCfg:
"""Actions for MindRobot left arm IK teleoperation."""
# Left arm IK control
arm_action = DifferentialInverseKinematicsActionCfg(
asset_name="robot",
joint_names=["l_joint[1-6]"],
body_name="Link_6", # Last link of left arm
controller=DifferentialIKControllerCfg(
command_type="pose",
use_relative_mode=True,
ik_method="dls",
),
scale=0.5,
body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(
pos=[0.0, 0.0, 0.0]
),
)
# Left gripper control (binary: open/close)
gripper_action = BinaryJointPositionActionCfg(
asset_name="robot",
joint_names=["left_hand_joint_left", "left_hand_joint_right"],
open_command_expr={
"left_hand_joint_left": 0.0,
"left_hand_joint_right": 0.0,
},
close_command_expr={
"left_hand_joint_left": -0.03,
"left_hand_joint_right": 0.03,
},
)
# =====================================================================
# Observations Configuration
# =====================================================================
@configclass
class MindRobotTeleopObsCfg:
"""Minimal observations for MindRobot teleoperation."""
@configclass
class PolicyCfg(ObsGroup):
"""Observations for policy group - only robot state, no cube dependencies."""
actions = ObsTerm(func=mdp.last_action)
joint_pos = ObsTerm(func=mdp.joint_pos_rel)
joint_vel = ObsTerm(func=mdp.joint_vel_rel)
eef_pos = ObsTerm(func=mdp.ee_frame_pos)
eef_quat = ObsTerm(func=mdp.ee_frame_quat)
gripper_pos = ObsTerm(func=mdp.gripper_pos)
def __post_init__(self):
self.enable_corruption = False
self.concatenate_terms = False
# observation groups
policy: PolicyCfg = PolicyCfg()
# =====================================================================
# Terminations Configuration
# =====================================================================
@configclass
class MindRobotTeleopTerminationsCfg:
"""Minimal terminations for teleoperation - only time_out or none."""
# Optional: Keep time_out for safety, or remove entirely
time_out = DoneTerm(func=mdp.time_out, time_out=True)
# =====================================================================
# Main Environment Configuration
# =====================================================================
@configclass
class EmptyCfg:
pass
@configclass
class MindRobotLeftArmIKEnvCfg(ManagerBasedRLEnvCfg):
"""MindRobot 左臂 IK-Rel 遥操作环境配置(最小化版本)。"""
# Scene settings
scene: MindRobotTeleopSceneCfg = MindRobotTeleopSceneCfg(
num_envs=1,
env_spacing=2.5,
)
# Basic settings
observations: MindRobotTeleopObsCfg = MindRobotTeleopObsCfg()
actions: MindRobotTeleopActionsCfg = MindRobotTeleopActionsCfg()
# MDP settings
terminations: MindRobotTeleopTerminationsCfg = MindRobotTeleopTerminationsCfg()
# Unused managers
commands: EmptyCfg = EmptyCfg()
rewards: EmptyCfg = EmptyCfg()
events: EmptyCfg = EmptyCfg()
curriculum: EmptyCfg = EmptyCfg()
# XR configuration for hand tracking (if needed)
xr: XrCfg = XrCfg(
anchor_pos=(-0.1, -0.5, -1.05),
anchor_rot=(0.866, 0, 0, -0.5),
)
def __post_init__(self):
"""Post initialization."""
super().__post_init__()
# General settings
self.decimation = 2
self.episode_length_s = 50.0
# Simulation settings
self.sim.dt = 0.01 # 100Hz
self.sim.render_interval = 2
# Set MindRobot
self.scene.robot = MINDBOT_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
# Configure end-effector frame transformer
marker_cfg = FRAME_MARKER_CFG.copy()
marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1)
marker_cfg.prim_path = "/Visuals/FrameTransformer"
self.scene.ee_frame = FrameTransformerCfg(
prim_path="{ENV_REGEX_NS}/Robot/rm_65_fb_left/base_link",
debug_vis=False,
visualizer_cfg=marker_cfg,
target_frames=[
FrameTransformerCfg.FrameCfg(
prim_path="{ENV_REGEX_NS}/Robot/rm_65_fb_left/Link_6",
name="end_effector",
offset=OffsetCfg(
pos=[0.0, 0.0, 0.0],
),
),
],
)
# Configure teleoperation devices
self.teleop_devices = DevicesCfg(
devices={
"keyboard": Se3KeyboardCfg(
pos_sensitivity=0.05,
rot_sensitivity=0.05,
sim_device=self.sim.device,
),
"spacemouse": Se3SpaceMouseCfg(
pos_sensitivity=0.05,
rot_sensitivity=0.05,
sim_device=self.sim.device,
),
"gamepad": Se3GamepadCfg(
pos_sensitivity=0.1,
rot_sensitivity=0.1,
sim_device=self.sim.device,
),
}
)
# Gripper parameters for status checking
self.gripper_joint_names = ["left_hand_joint_left", "left_hand_joint_right"]
self.gripper_open_val = 0.0
self.gripper_threshold = 0.005

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# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Reinforcement learning tasks."""
import gymnasium as gym # noqa: F401