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yt lee
2026-03-06 17:31:00 +08:00
parent 36b4273582
commit 2b290d805a
3 changed files with 667 additions and 492 deletions
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216
scripts/environments/teleoperation/teleop_xr_agent.py
View File

@@ -45,6 +45,14 @@ parser.add_argument(
choices=["left", "right"],
help="Which arm/controller to use.",
)
parser.add_argument(
"--base_speed", type=float, default=3.0,
help="Max wheel speed (rad/s) for joystick full forward.",
)
parser.add_argument(
"--base_turn", type=float, default=2.0,
help="Max wheel differential (rad/s) for joystick full left/right.",
)
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
@@ -78,6 +86,31 @@ from xr_utils.geometry import R_HEADSET_TO_WORLD
# Teleoperation Interface for XR
# =====================================================================
def _quat_wxyz_to_rotation(quat_wxyz: np.ndarray) -> R:
"""Convert Isaac-style wxyz quaternion to scipy Rotation."""
return R.from_quat([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]])
def _rotation_to_quat_wxyz(rot: R) -> np.ndarray:
"""Convert scipy Rotation quaternion to Isaac-style wxyz."""
quat_xyzw = rot.as_quat()
return np.array([quat_xyzw[3], quat_xyzw[0], quat_xyzw[1], quat_xyzw[2]])
def world_pose_to_root_frame(
pos_w: np.ndarray,
quat_wxyz: np.ndarray,
root_pos_w: np.ndarray,
root_quat_wxyz: np.ndarray,
) -> tuple[np.ndarray, np.ndarray]:
"""Express a world-frame pose in the robot root frame."""
root_rot = _quat_wxyz_to_rotation(root_quat_wxyz)
pose_rot = _quat_wxyz_to_rotation(quat_wxyz)
pos_root = root_rot.inv().apply(pos_w - root_pos_w)
quat_root = _rotation_to_quat_wxyz(root_rot.inv() * pose_rot)
return pos_root, quat_root
class XrTeleopController:
"""Teleop controller for PICO XR headset."""
@@ -105,6 +138,10 @@ class XrTeleopController:
self.grip_active = False
self.frame_count = 0
self.reset_button_latched = False
self.require_grip_reengage = False
self.grip_engage_threshold = 0.8
self.grip_release_threshold = 0.2
# Callbacks (like reset, etc)
self.callbacks = {}
@@ -117,6 +154,11 @@ class XrTeleopController:
self.prev_xr_quat = None
self.grip_active = False
self.frame_count = 0
self.target_eef_pos = None
self.target_eef_quat = None
# Require one grip release after reset so stale controller motion
# cannot immediately drive the robot back toward the previous pose.
self.require_grip_reengage = True
def close(self):
self.xr_client.close()
@@ -144,14 +186,17 @@ class XrTeleopController:
Reads the XR controller.
Relative bounds return 7D action tensor: [dx, dy, dz, drx, dry, drz, gripper]
Absolute bounds return 8D action tensor: [x, y, z, qw, qx, qy, qz, gripper]
Note: current_eef_quat expects wxyz.
Note: in absolute mode current_eef_* and the returned target are in WORLD frame.
The caller is responsible for converting to root frame before sending to IK.
"""
# XR buttons check (e.g. A or B for reset)
try:
if self.xr_client.get_button("B") or self.xr_client.get_button("Y"):
reset_pressed = self.xr_client.get_button("B") or self.xr_client.get_button("Y")
if reset_pressed and not self.reset_button_latched:
if "RESET" in self.callbacks:
self.callbacks["RESET"]()
except:
self.reset_button_latched = reset_pressed
except Exception:
pass
try:
@@ -165,11 +210,23 @@ class XrTeleopController:
if not is_valid_quaternion(raw_pose[3:]):
return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
# Transform XR pose pos directly via Matrix mapping for safety check (though we can map diffs next)
pos_w = self.R_headset_world @ raw_pose[:3]
if self.require_grip_reengage:
if grip <= self.grip_release_threshold:
self.require_grip_reengage = False
else:
if self.is_absolute and current_eef_pos is not None and current_eef_quat is not None:
self.target_eef_pos = current_eef_pos.copy()
self.target_eef_quat = current_eef_quat.copy()
return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
# Use hysteresis so noisy analog grip values do not accidentally re-enable teleop.
if self.grip_active:
grip_pressed = grip > self.grip_release_threshold
else:
grip_pressed = grip >= self.grip_engage_threshold
# 握持键作为离合器 (Clutch)
if grip < 0.5:
if not grip_pressed:
self.prev_xr_pos = None
self.prev_xr_quat = None
self.grip_active = False
@@ -197,11 +254,9 @@ class XrTeleopController:
# 2. Extract Delta POS in World frame
world_delta_pos = (xr_world_pos - prev_xr_world_pos) * self.pos_sensitivity
pos_norm = np.linalg.norm((raw_pose[:3] - self.prev_xr_pos))
# 3. Extract Delta ROT in World frame
world_delta_rot = quat_diff_as_rotvec_xyzw(prev_xr_world_quat_xyzw, xr_world_quat_xyzw) * self.rot_sensitivity
rot_norm = np.linalg.norm(quat_diff_as_rotvec_xyzw(self.prev_xr_quat, raw_pose[3:]))
# 4. Gripper
gripper_action = 1.0 if trigger > 0.5 else -1.0
@@ -211,16 +266,12 @@ class XrTeleopController:
self.target_eef_pos = np.zeros(3)
self.target_eef_quat = np.array([1.0, 0.0, 0.0, 0.0])
# Position update (simple translation in world frame)
# Accumulate in world frame so VR direction always matches sim direction.
self.target_eef_pos += world_delta_pos
# Rotation update: apply delta_R (in world frame) to target_R (in world frame)
# R_new = R_delta @ R_target
target_r = R.from_quat([self.target_eef_quat[1], self.target_eef_quat[2], self.target_eef_quat[3], self.target_eef_quat[0]])
target_r = _quat_wxyz_to_rotation(self.target_eef_quat)
delta_r = R.from_rotvec(world_delta_rot)
new_r = delta_r * target_r
new_rot_xyzw = new_r.as_quat()
self.target_eef_quat = np.array([new_rot_xyzw[3], new_rot_xyzw[0], new_rot_xyzw[1], new_rot_xyzw[2]])
self.target_eef_quat = _rotation_to_quat_wxyz(delta_r * target_r)
action = torch.tensor([
self.target_eef_pos[0], self.target_eef_pos[1], self.target_eef_pos[2],
@@ -231,14 +282,15 @@ class XrTeleopController:
self.prev_xr_quat = raw_pose[3:].copy()
else:
# Relative clamping limits (used by relative mode to avoid divergence)
max_pos_delta = 0.04
if pos_norm > max_pos_delta:
world_delta_pos = (world_delta_pos * max_pos_delta / pos_norm)
max_pos_delta = 0.05
world_pos_norm = np.linalg.norm(world_delta_pos)
if world_pos_norm > max_pos_delta:
world_delta_pos = world_delta_pos * (max_pos_delta / world_pos_norm)
max_rot_delta = 0.02
if rot_norm > max_rot_delta:
world_delta_rot = (world_delta_rot * max_rot_delta / rot_norm)
max_rot_delta = 0.15
world_rot_norm = np.linalg.norm(world_delta_rot)
if world_rot_norm > max_rot_delta:
world_delta_rot = world_delta_rot * (max_rot_delta / world_rot_norm)
action = torch.tensor([
world_delta_pos[0], world_delta_pos[1], world_delta_pos[2],
@@ -256,11 +308,11 @@ class XrTeleopController:
print(f"| Task Mode: {'ABSOLUTE' if self.is_absolute else 'RELATIVE'}")
print(f"| Raw VR Pos (OpenXR): {np.array(raw_pose[:3])}")
print(f"| Raw VR Quat (xyzw): {np.array(raw_pose[3:])}")
print(f"| XR Delta Pos (world): {world_delta_pos} (norm={pos_norm:.4f})")
print(f"| XR Delta Rot (world): {world_delta_rot} (norm={rot_norm:.4f})")
print(f"| XR Delta Pos (world): {world_delta_pos} (norm={np.linalg.norm(world_delta_pos):.4f})")
print(f"| XR Delta Rot (world): {world_delta_rot} (norm={np.linalg.norm(world_delta_rot):.4f})")
if self.is_absolute:
print(f"| Targ Pos (dx,dy,dz): {action[:3].numpy()}")
print(f"| Targ Quat (wxyz): {action[3:7].numpy()}")
print(f"| Targ Pos (world): {action[:3].numpy()}")
print(f"| Targ Quat (world, wxyz): {action[3:7].numpy()}")
else:
print(f"| Sent Action Pos (dx,dy,dz): {action[:3].numpy()}")
print(f"| Sent Action Rot (rx,ry,rz): {action[3:6].numpy()}")
@@ -313,30 +365,114 @@ def main() -> None:
teleop_interface.add_callback("RESET", request_reset)
def get_arm_action_term():
return env.action_manager._terms["arm_action"]
def clear_ik_target_state():
"""Clear the internal IK target so reset does not reuse the previous pose command."""
if not is_abs_mode:
return
arm_action_term = get_arm_action_term()
ee_pos_b, ee_quat_b = arm_action_term._compute_frame_pose()
arm_action_term._raw_actions.zero_()
arm_action_term._processed_actions.zero_()
arm_action_term._ik_controller._command.zero_()
arm_action_term._ik_controller.ee_pos_des[:] = ee_pos_b
arm_action_term._ik_controller.ee_quat_des[:] = ee_quat_b
def convert_action_world_to_root(action_tensor: torch.Tensor) -> torch.Tensor:
"""Convert an absolute IK action from world frame to robot root frame."""
robot = env.unwrapped.scene["robot"]
root_pos_w = robot.data.root_pos_w[0].detach().cpu().numpy()
root_quat_w = robot.data.root_quat_w[0].detach().cpu().numpy() # wxyz
target_pos_w = action_tensor[:3].numpy()
target_quat_w = action_tensor[3:7].numpy()
pos_root, quat_root = world_pose_to_root_frame(
target_pos_w, target_quat_w, root_pos_w, root_quat_w,
)
out = action_tensor.clone()
out[:3] = torch.tensor(pos_root, dtype=torch.float32)
out[3:7] = torch.tensor(quat_root, dtype=torch.float32)
return out
def get_wheel_action() -> torch.Tensor:
"""Read left joystick and return 4-DOF wheel velocity command.
Skid-steer differential drive.
Joystick: Y-axis (+1 = forward), X-axis (+1 = right turn).
Joint order from articulation (terminal log):
[right_b, left_b, left_f, right_f]
Right/left sign convention assumes both sides' joints have the same
axis direction (positive velocity = forward). If the robot drives
backward when pushing forward, negate base_speed in the launch command.
"""
try:
joy = teleop_interface.xr_client.get_joystick("left")
jy = float(joy[1]) # forward / backward
jx = float(joy[0]) # right / left
except Exception:
return torch.zeros(4)
v = jy * args_cli.base_speed
omega = jx * args_cli.base_turn
# Positive omega = turn right → left wheels faster, right wheels slower
right_vel = v - omega
left_vel = v + omega
return torch.tensor(
[right_vel, left_vel, left_vel, right_vel], dtype=torch.float32
)
env.reset()
clear_ik_target_state()
teleop_interface.reset()
print("\n" + "=" * 50)
print(" 🚀 Teleoperation Started!")
print(" 🎮 Use the TRIGGER to open/close gripper.")
print(" ✊ Hold GRIP button and move the controller to move the arm.")
print(" 🕹️ Press B or Y to reset the environment.")
print(" ✊ Hold GRIP and move the controller to move the arm.")
print(" 🕹️ Left joystick: Y=forward/back, X=turn left/right.")
print(" 🔄 Press B or Y to reset the environment.")
print("=" * 50 + "\n")
device = env.unwrapped.device
sim_frame = 0
obs, _ = env.reset()
clear_ik_target_state()
while simulation_app.is_running():
try:
with torch.inference_mode():
# Extract tracking pose to seed absolute IK
if should_reset:
obs, _ = env.reset()
clear_ik_target_state()
teleop_interface.reset()
should_reset = False
sim_frame = 0
continue
# Read current EEF in world frame from observations.
policy_obs = obs["policy"]
eef_pos = policy_obs["eef_pos"][0].cpu().numpy()
eef_quat = policy_obs["eef_quat"][0].cpu().numpy()
# Get action from XR Controller
action_np = teleop_interface.advance(current_eef_pos=eef_pos, current_eef_quat=eef_quat)
# Get action from XR Controller (world frame for absolute mode).
action_np = teleop_interface.advance(
current_eef_pos=eef_pos, current_eef_quat=eef_quat,
)
# IK expects root-frame commands; convert just before sending.
if is_abs_mode:
action_np = convert_action_world_to_root(action_np)
# Action manager order: arm_action | wheel_action | gripper_action
# arm=7, wheel=4, gripper=1 → total 12 dims.
wheel_np = get_wheel_action()
action_np = torch.cat([action_np[:7], wheel_np, action_np[7:]])
actions = action_np.unsqueeze(0).repeat(env.num_envs, 1).to(device)
# Step environment
@@ -363,30 +499,32 @@ def main() -> None:
print(f" [{i:2d}] {name:30s} = {joint_pos[i]:+.4f}")
print(f"{'='*70}")
# Find arm joint indices dynamically by looking at the first 6-7 joints that aren't fingers or hands
arm_idx = [i for i, n in enumerate(jnames) if "finger" not in n and "hand" not in n][:7]
print(f" Deduced arm indices: {arm_idx}")
arm_idx = [i for i, n in enumerate(jnames) if n.startswith("l_joint")]
print(f" Deduced left arm indices: {arm_idx}")
print(f"{'='*70}\n")
# Get arm indices (cache-friendly: find once)
if not hasattr(env, '_arm_idx_cache'):
robot = env.unwrapped.scene["robot"]
jnames = robot.joint_names
env._arm_idx_cache = [i for i, n in enumerate(jnames) if "finger" not in n and "hand" not in n][:7]
env._arm_idx_cache = [i for i, n in enumerate(jnames) if n.startswith("l_joint")]
arm_idx = env._arm_idx_cache
arm_joints = joint_pos[arm_idx]
try:
joy_dbg = teleop_interface.xr_client.get_joystick("left")
joy_str = f"[{joy_dbg[0]:+.2f}, {joy_dbg[1]:+.2f}]"
except Exception:
joy_str = "N/A"
print(f"\n---------------- [ROBOT STATE frame={sim_frame}] ----------------")
print(f"| Left Arm Joints (rad): {arm_joints}")
print(f"| EEF Pos (world): {eef_pos}")
print(f"| EEF Quat (world, wxyz): {eef_quat}")
print(f"| Last Action Sent: {last_act}")
print(f"| Joystick (x=turn,y=fwd): {joy_str}")
print(f"----------------------------------------------------------------")
if should_reset:
env.reset()
teleop_interface.reset()
should_reset = False
except Exception as e:
logger.error(f"Error during simulation step: {e}")
break

271
source/mindbot/mindbot/tasks/manager_based/il/open_drybox/mindrobot_cfg.py
View File

@@ -1,136 +1,135 @@
# Copyright (c) 2024, MindRobot Project
# SPDX-License-Identifier: BSD-3-Clause
"""Configuration for MindRobot dual-arm robot.
Arms are RealMan RM-65 (6-DOF, 5kg payload, 7.2kg self-weight, 610mm reach).
Reference: https://develop.realman-robotics.com/robot4th/robotParameter/RM65OntologyParameters/
The following configurations are available:
* :obj:`MINDBOT_CFG`: Base configuration with gravity enabled (general purpose).
* :obj:`MINDBOT_HIGH_PD_CFG`: Gravity disabled, stiffer PD for IK task-space control.
"""
import isaaclab.sim as sim_utils
from isaaclab.actuators import ImplicitActuatorCfg
from isaaclab.assets import ArticulationCfg
from mindbot.utils.assets import MINDBOT_ASSETS_DIR
MINDBOT_CFG = ArticulationCfg(
spawn=sim_utils.UsdFileCfg(
usd_path=f"{MINDBOT_ASSETS_DIR}/robots/mindbot/mindbot_cd2.usd",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
max_depenetration_velocity=5.0,
),
articulation_props=sim_utils.ArticulationRootPropertiesCfg(
fix_root_link=False,
enabled_self_collisions=True,
solver_position_iteration_count=8,
solver_velocity_iteration_count=0,
),
),
init_state=ArticulationCfg.InitialStateCfg(
joint_pos={
# ====== Left arm (RM-65) — away from singularities ======
# Elbow singularity: q3=0; Wrist singularity: q5=0.
# The pose below keeps q3≠0 and q5≠0.
"l_joint1": 1.5708, # 135°
"l_joint2": -1.5708, # -70°
"l_joint3": -1.5708, # -90° (away from elbow singularity q3=0)
"l_joint4": 1.5708, # 90°
"l_joint5": 1.5708, # 90° (away from wrist singularity q5=0)
"l_joint6": -1.5708, # -70°
# ====== Right arm (RM-65) ======
"r_joint1": -2.3562,
"r_joint2": -1.2217,
"r_joint3": 1.5708,
"r_joint4": -1.5708,
"r_joint5": -1.5708,
"r_joint6": 1.2217,
# ====== Grippers (0=open) ======
"left_hand_joint_left": 0.0,
"left_hand_joint_right": 0.0,
"right_hand_joint_left": 0.0,
"right_hand_joint_right": 0.0,
# ====== Trunk & Head ======
"PrismaticJoint": 0.26,
"head_revoluteJoint": 0.0,
},
pos=(0.0, 0.0, 0.0),
),
actuators={
# RM-65 J1-J3: shoulder/elbow — max 180°/s ≈ 3.14 rad/s
"left_arm_shoulder": ImplicitActuatorCfg(
joint_names_expr=["l_joint[1-3]"],
effort_limit_sim=50.0,
velocity_limit_sim=3.14,
stiffness=80.0,
damping=4.0,
),
# RM-65 J4-J6: wrist — max 225°/s ≈ 3.93 rad/s
"left_arm_wrist": ImplicitActuatorCfg(
joint_names_expr=["l_joint[4-6]"],
effort_limit_sim=20.0,
velocity_limit_sim=3.93,
stiffness=80.0,
damping=4.0,
),
"right_arm_shoulder": ImplicitActuatorCfg(
joint_names_expr=["r_joint[1-3]"],
effort_limit_sim=50.0,
velocity_limit_sim=3.14,
stiffness=80.0,
damping=4.0,
),
"right_arm_wrist": ImplicitActuatorCfg(
joint_names_expr=["r_joint[4-6]"],
effort_limit_sim=20.0,
velocity_limit_sim=3.93,
stiffness=80.0,
damping=4.0,
),
"head": ImplicitActuatorCfg(
joint_names_expr=["head_revoluteJoint"],
effort_limit_sim=12.0,
stiffness=80.0,
damping=4.0,
),
"trunk": ImplicitActuatorCfg(
joint_names_expr=["PrismaticJoint"],
effort_limit_sim=200.0,
velocity_limit_sim=0.2,
stiffness=1000.0,
damping=100.0,
),
"wheels": ImplicitActuatorCfg(
joint_names_expr=[".*_revolute_Joint"],
effort_limit_sim=200.0,
stiffness=0.0,
damping=100.0,
),
"grippers": ImplicitActuatorCfg(
joint_names_expr=[".*_hand_joint.*"],
effort_limit_sim=200.0,
stiffness=2e3,
damping=1e2,
),
},
soft_joint_pos_limit_factor=1.0,
)
"""Base configuration for MindRobot. Gravity enabled, low PD gains."""
MINDBOT_HIGH_PD_CFG = MINDBOT_CFG.copy()
MINDBOT_HIGH_PD_CFG.spawn.rigid_props.disable_gravity = True
MINDBOT_HIGH_PD_CFG.actuators["left_arm_shoulder"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm_shoulder"].damping = 80.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm_wrist"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm_wrist"].damping = 80.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_shoulder"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_shoulder"].damping = 80.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_wrist"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_wrist"].damping = 80.0
"""IK task-space control configuration. Gravity disabled, stiffer PD for tracking."""
# Copyright (c) 2024, MindRobot Project
# SPDX-License-Identifier: BSD-3-Clause
"""Configuration for MindRobot dual-arm robot.
Arms are RealMan RM-65 (6-DOF, 5kg payload, 7.2kg self-weight, 610mm reach).
Reference: https://develop.realman-robotics.com/robot4th/robotParameter/RM65OntologyParameters/
The following configurations are available:
* :obj:`MINDBOT_CFG`: Base configuration with gravity enabled (general purpose).
* :obj:`MINDBOT_HIGH_PD_CFG`: Gravity disabled, stiffer PD for IK task-space control.
"""
import isaaclab.sim as sim_utils
from isaaclab.actuators import ImplicitActuatorCfg
from isaaclab.assets import ArticulationCfg
from mindbot.utils.assets import MINDBOT_ASSETS_DIR
MINDBOT_CFG = ArticulationCfg(
spawn=sim_utils.UsdFileCfg(
usd_path=f"{MINDBOT_ASSETS_DIR}/robots/mindbot/mindbot_cd2.usd",
rigid_props=sim_utils.RigidBodyPropertiesCfg(
disable_gravity=False,
max_depenetration_velocity=5.0,
),
articulation_props=sim_utils.ArticulationRootPropertiesCfg(
fix_root_link=False,
enabled_self_collisions=True,
solver_position_iteration_count=8,
solver_velocity_iteration_count=0,
),
),
init_state=ArticulationCfg.InitialStateCfg(
joint_pos={
# ====== Left arm (RM-65) — away from singularities ======
# Elbow singularity: q3=0; Wrist singularity: q5=0.
# The pose below keeps q3≠0 and q5≠0.
"l_joint1": 1.5708, # 135°
"l_joint2": -1.5708, # -70°
"l_joint3": -1.5708, # -90° (away from elbow singularity q3=0)
"l_joint4": 1.5708, # 90°
"l_joint5": 1.5708, # 90° (away from wrist singularity q5=0)
"l_joint6": -1.5708, # -70°
# ====== Right arm (RM-65) ======
"r_joint1": -2.3562,
"r_joint2": -1.2217,
"r_joint3": 1.5708,
"r_joint4": -1.5708,
"r_joint5": -1.5708,
"r_joint6": 1.2217,
# ====== Grippers (0=open) ======
"left_hand_joint_left": 0.0,
"left_hand_joint_right": 0.0,
"right_hand_joint_left": 0.0,
"right_hand_joint_right": 0.0,
# ====== Trunk & Head ======
"PrismaticJoint": 0.26,
"head_revoluteJoint": 0.0,
},
pos=(0.0, 0.0, 0.7),
),
actuators={
# RM-65 J1-J3: shoulder/elbow — max 180°/s ≈ 3.14 rad/s
"left_arm_shoulder": ImplicitActuatorCfg(
joint_names_expr=["l_joint[1-3]"],
effort_limit_sim=50.0,
velocity_limit_sim=3.14,
stiffness=80.0,
damping=4.0,
),
# RM-65 J4-J6: wrist — max 225°/s ≈ 3.93 rad/s
"left_arm_wrist": ImplicitActuatorCfg(
joint_names_expr=["l_joint[4-6]"],
effort_limit_sim=20.0,
velocity_limit_sim=3.93,
stiffness=80.0,
damping=4.0,
),
"right_arm_shoulder": ImplicitActuatorCfg(
joint_names_expr=["r_joint[1-3]"],
effort_limit_sim=50.0,
velocity_limit_sim=3.14,
stiffness=80.0,
damping=4.0,
),
"right_arm_wrist": ImplicitActuatorCfg(
joint_names_expr=["r_joint[4-6]"],
effort_limit_sim=20.0,
velocity_limit_sim=3.93,
stiffness=80.0,
damping=4.0,
),
"head": ImplicitActuatorCfg(
joint_names_expr=["head_revoluteJoint"],
effort_limit_sim=12.0,
stiffness=80.0,
damping=4.0,
),
"trunk": ImplicitActuatorCfg(
joint_names_expr=["PrismaticJoint"],
effort_limit_sim=200.0,
velocity_limit_sim=0.2,
stiffness=1000.0,
damping=100.0,
),
"wheels": ImplicitActuatorCfg(
joint_names_expr=[".*_revolute_Joint"],
effort_limit_sim=200.0,
stiffness=0.0,
damping=100.0,
),
"grippers": ImplicitActuatorCfg(
joint_names_expr=[".*_hand_joint.*"],
effort_limit_sim=200.0,
stiffness=2e3,
damping=1e2,
),
},
soft_joint_pos_limit_factor=1.0,
)
"""Base configuration for MindRobot. Gravity enabled, low PD gains."""
MINDBOT_HIGH_PD_CFG = MINDBOT_CFG.copy()
MINDBOT_HIGH_PD_CFG.actuators["left_arm_shoulder"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm_shoulder"].damping = 80.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm_wrist"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["left_arm_wrist"].damping = 80.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_shoulder"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_shoulder"].damping = 80.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_wrist"].stiffness = 400.0
MINDBOT_HIGH_PD_CFG.actuators["right_arm_wrist"].damping = 80.0
"""IK task-space control configuration. Gravity disabled, stiffer PD for tracking."""

672
source/mindbot/mindbot/tasks/manager_based/il/open_drybox/mindrobot_left_arm_ik_env_cfg.py
View File

@@ -1,317 +1,355 @@
# 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 torch
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 EventTermCfg as EventTerm
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
from mindbot.utils.assets import MINDBOT_ASSETS_DIR
# # 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, 0.0, 0.0]),
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)),
)
# room = AssetBaseCfg(
# prim_path="{ENV_REGEX_NS}/Room",
# spawn=sim_utils.UsdFileCfg(
# usd_path=f"{MINDBOT_ASSETS_DIR}/twinlab/Collected_lab2/lab.usd",
# ),
# )
# 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="left_hand_body",
controller=DifferentialIKControllerCfg(
command_type="pose",
use_relative_mode=True,
ik_method="dls",
),
scale=1,
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)
# =====================================================================
# Events Configuration
# =====================================================================
@configclass
class MindRobotTeleopEventsCfg:
"""Reset events for teleoperation: R键重置时将场景恢复到初始状态。"""
reset_scene = EventTerm(
func=mdp.reset_scene_to_default,
mode="reset",
)
# =====================================================================
# 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()
events: MindRobotTeleopEventsCfg = MindRobotTeleopEventsCfg()
# Unused managers
commands: EmptyCfg = EmptyCfg()
rewards: 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 with FIXED root for teleoperation
# The original MINDBOT_HIGH_PD_CFG has fix_root_link=False (mobile base).
# For teleoperation, the base MUST be fixed to prevent the whole robot
# from sliding/tipping when IK applies torques to the arm joints.
robot_cfg = MINDBOT_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
robot_cfg.spawn.articulation_props.fix_root_link = True
self.scene.robot = robot_cfg
# 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/l_hand_01/left_hand_body",
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
@configclass
class MindRobotLeftArmIKAbsEnvCfg(MindRobotLeftArmIKEnvCfg):
"""MindRobot 左臂 IK-Abs 遥操作环境配置（绝对坐标系，适合 VR 控制器）。"""
def __post_init__(self):
super().__post_init__()
# Switch controller to absolute mode
self.actions.arm_action.controller.use_relative_mode = False
# 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 torch
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,
JointVelocityActionCfg,
)
from isaaclab.managers import EventTermCfg as EventTerm
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
from mindbot.utils.assets import MINDBOT_ASSETS_DIR
# # 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, 0.0, 0.0]),
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)),
)
# room = AssetBaseCfg(
# prim_path="{ENV_REGEX_NS}/Room",
# spawn=sim_utils.UsdFileCfg(
# usd_path=f"{MINDBOT_ASSETS_DIR}/twinlab/Collected_lab2/lab.usd",
# ),
# )
# 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="left_hand_body",
controller=DifferentialIKControllerCfg(
command_type="pose",
use_relative_mode=True,
ik_method="dls",
),
scale=1,
body_offset=DifferentialInverseKinematicsActionCfg.OffsetCfg(
pos=[0.0, 0.0, 0.0],
),
)
# Wheel velocity control for differential drive (skid-steer).
# Joint order in articulation: right_b, left_b, left_f, right_f
# (from terminal joint index listing: [2],[3],[4],[5]).
# Action vector: [right_b_vel, left_b_vel, left_f_vel, right_f_vel] in rad/s.
wheel_action = JointVelocityActionCfg(
asset_name="robot",
joint_names=[".*_revolute_Joint"],
scale=1.0,
)
# 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)
# =====================================================================
# Events Configuration
# =====================================================================
def _disable_arm_gravity(env, env_ids: torch.Tensor):
"""Disable gravity for both arm subtrees; chassis/wheels/trunk keep gravity.
Called once at startup. The @apply_nested decorator on
modify_rigid_body_properties recurses into all rigid-body children under
the given prim path, so every link of the arm (including gripper) is covered.
Arm prim roots (per env):
Robot/rm_65_fb_left — left RM-65 arm + gripper
Robot/rm_65_fb_right — right RM-65 arm + gripper
"""
import isaaclab.sim.schemas as schemas
arm_cfg = RigidBodyPropertiesCfg(disable_gravity=True)
for env_id in range(env.num_envs):
for arm_path in [
f"/World/envs/env_{env_id}/Robot/rm_65_fb_left",
f"/World/envs/env_{env_id}/Robot/rm_65_b_right",
]:
schemas.modify_rigid_body_properties(arm_path, arm_cfg)
@configclass
class MindRobotTeleopEventsCfg:
"""Reset events for teleoperation: R键重置时将场景恢复到初始状态。"""
disable_arm_gravity = EventTerm(
func=_disable_arm_gravity,
mode="startup",
)
reset_scene = EventTerm(
func=mdp.reset_scene_to_default,
mode="reset",
)
# =====================================================================
# 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()
events: MindRobotTeleopEventsCfg = MindRobotTeleopEventsCfg()
# Unused managers
commands: EmptyCfg = EmptyCfg()
rewards: 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
# Keep the base mobile for teleoperation.
# The absolute arm IK command must therefore be expressed in the robot
# root frame instead of assuming a fixed world-aligned base.
robot_cfg = MINDBOT_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
robot_cfg.spawn.articulation_props.fix_root_link = False
self.scene.robot = robot_cfg
# 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/l_hand_01/left_hand_body",
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
@configclass
class MindRobotLeftArmIKAbsEnvCfg(MindRobotLeftArmIKEnvCfg):
"""MindRobot 左臂 IK-Abs 遥操作环境配置（绝对坐标系，适合 VR 控制器）。"""
def __post_init__(self):
super().__post_init__()
# Switch controller to absolute mode
self.actions.arm_action.controller.use_relative_mode = False