dual arm XR teleoperation: env cfg, shared client, joint locking
- Add MindRobotDualArmIKAbsEnvCfg: standalone dual-arm env inheriting ManagerBasedRLEnvCfg directly (no single-arm dependency), 20D action space (left_arm7 | wheel4 | left_gripper1 | right_arm7 | right_gripper1) - Add local mdp/ with parameterized gripper_pos(joint_names) to support independent left/right gripper observations without modifying IsaacLab - Update teleop_xr_agent.py for dual-arm mode: shared XrClient to avoid SDK double-init crash, root-frame IK command caching so arm joints are locked when grip not pressed (EEF world pos still tracks chassis) - Tune mindrobot_cfg.py initial poses with singularity-avoiding offsets - Add CLAUDE.md project instructions and debug_action_assembly.py Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
113
scripts/debug_action_assembly.py
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113
scripts/debug_action_assembly.py
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@@ -0,0 +1,113 @@
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#!/usr/bin/env python3
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"""
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验证 teleop_xr_agent.py 中相对模式下动作拼接顺序的正确性。
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无需 Isaac Sim / XR 设备，纯 CPU 运行。
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运行方式：
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python scripts/debug_action_assembly.py
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"""
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import torch
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# ============================================================
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# 模拟 Action Manager 的切分逻辑
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# ============================================================
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# 来自 mindrobot_left_arm_ik_env_cfg.py 的 Action 配置：
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# arm_action : DifferentialInverseKinematicsActionCfg，相对模式 6D，绝对模式 7D
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# wheel_action: JointVelocityActionCfg，4 个轮子，4D
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# gripper_action: BinaryJointPositionActionCfg，1D
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def action_manager_split(action: torch.Tensor, arm_size: int):
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"""模拟 Isaac Lab ManagerBasedRLEnv 的动作切分。"""
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assert action.shape[-1] == arm_size + 4 + 1, \
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f"期望总维度 {arm_size + 4 + 1}，实际 {action.shape[-1]}"
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arm = action[..., :arm_size]
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wheel = action[..., arm_size:arm_size + 4]
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gripper = action[..., arm_size + 4:]
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return arm, wheel, gripper
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# ============================================================
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# 复现 teleop_xr_agent.py:474 的拼接逻辑
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# ============================================================
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def current_assembly(action_np: torch.Tensor, wheel_np: torch.Tensor) -> torch.Tensor:
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"""现有代码的拼接逻辑（逐字复制自 teleop_xr_agent.py:474）。"""
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return torch.cat([action_np[:7], wheel_np, action_np[7:]])
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def fixed_assembly(action_np: torch.Tensor, wheel_np: torch.Tensor, is_abs_mode: bool) -> torch.Tensor:
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"""修正后的拼接逻辑。"""
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arm_size = 7 if is_abs_mode else 6
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arm_action = action_np[:arm_size]
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gripper_val = action_np[arm_size:] # 最后一个元素
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return torch.cat([arm_action, wheel_np, gripper_val])
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# ============================================================
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# 测试
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# ============================================================
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def run_test(mode: str):
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is_abs = (mode == "absolute")
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arm_size = 7 if is_abs else 6
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# 构造可辨识的测试值
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if is_abs:
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# 绝对模式：[x,y,z,qw,qx,qy,qz, gripper]
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fake_action = torch.tensor([0.1, 0.2, 0.3, # pos
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1.0, 0.0, 0.0, 0.0, # quat
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0.9]) # gripper (trigger=0.9)
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else:
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# 相对模式：[dx,dy,dz, drx,dry,drz, gripper]
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fake_action = torch.tensor([0.01, 0.02, 0.03, # delta pos
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0.05, 0.06, 0.07, # delta rot
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0.9]) # gripper (trigger=0.9)
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fake_wheel = torch.tensor([1.5, 1.5, 1.5, 1.5]) # 向前行驶
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print(f"\n{'='*60}")
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print(f" 测试模式: {mode.upper()}")
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print(f"{'='*60}")
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print(f" 原始 action_np : {fake_action.tolist()}")
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print(f" 原始 wheel_np : {fake_wheel.tolist()}")
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print(f" 期望 gripper 值 : {fake_action[-1].item()} (应为 0.9)")
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print(f" 期望 wheel 值 : {fake_wheel.tolist()} (应为 [1.5,1.5,1.5,1.5])")
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# ---- 现有代码 ----
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assembled_current = current_assembly(fake_action, fake_wheel)
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try:
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arm_c, wheel_c, grip_c = action_manager_split(assembled_current, arm_size)
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print(f"\n [现有代码]")
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print(f" 拼接结果({assembled_current.shape[0]}D) : {assembled_current.tolist()}")
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print(f" → arm ({arm_c.shape[0]}D) : {arm_c.tolist()}")
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print(f" → wheel ({wheel_c.shape[0]}D) : {wheel_c.tolist()}")
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print(f" → gripper ({grip_c.shape[0]}D) : {grip_c.tolist()}")
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arm_ok = True
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wheel_ok = torch.allclose(wheel_c, fake_wheel)
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grip_ok = torch.allclose(grip_c, fake_action[-1:])
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print(f" arm 正确? {'✅' if arm_ok else '❌'} | wheel 正确? {'✅' if wheel_ok else '❌ ← BUG'} | gripper 正确? {'✅' if grip_ok else '❌ ← BUG'}")
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except AssertionError as e:
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print(f" [现有代码] 维度断言失败: {e}")
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# ---- 修正后代码 ----
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assembled_fixed = fixed_assembly(fake_action, fake_wheel, is_abs)
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try:
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arm_f, wheel_f, grip_f = action_manager_split(assembled_fixed, arm_size)
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print(f"\n [修正后代码]")
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print(f" 拼接结果({assembled_fixed.shape[0]}D) : {assembled_fixed.tolist()}")
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print(f" → arm ({arm_f.shape[0]}D) : {arm_f.tolist()}")
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print(f" → wheel ({wheel_f.shape[0]}D) : {wheel_f.tolist()}")
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print(f" → gripper ({grip_f.shape[0]}D) : {grip_f.tolist()}")
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arm_ok = True
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wheel_ok = torch.allclose(wheel_f, fake_wheel)
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grip_ok = torch.allclose(grip_f, fake_action[-1:])
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print(f" arm 正确? {'✅' if arm_ok else '❌'} | wheel 正确? {'✅' if wheel_ok else '❌'} | gripper 正确? {'✅' if grip_ok else '❌'}")
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except AssertionError as e:
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print(f" [修正后代码] 维度断言失败: {e}")
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if __name__ == "__main__":
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run_test("relative")
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run_test("absolute")
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print()
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@@ -6,7 +6,11 @@
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"""
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Script to run teleoperation with Isaac Lab manipulation environments using PICO XR Controllers.
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This script uses XRoboToolkit to fetch XR controller poses and maps them to differential IK actions.
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Only absolute IK mode is supported (Isaac-MindRobot-LeftArm-IK-Abs-v0).
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The controller computes delta pose in Isaac Sim world frame and accumulates
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an absolute EEF target, which is then converted to robot root frame before
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being sent to the DifferentialIK action manager.
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"""
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import argparse
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@@ -24,44 +28,30 @@ logger = logging.getLogger(__name__)
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# add argparse arguments
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parser = argparse.ArgumentParser(
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description="Teleoperation for Isaac Lab environments with PICO XR Controller."
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)
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parser.add_argument(
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"--num_envs", type=int, default=1, help="Number of environments to simulate."
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description="Teleoperation for Isaac Lab environments with PICO XR Controller (absolute IK mode only)."
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)
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parser.add_argument("--num_envs", type=int, default=1, help="Number of environments to simulate.")
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parser.add_argument(
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"--task",
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type=str,
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default="Isaac-MindRobot-LeftArm-IK-Rel-v0",
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help="Name of the task.",
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default="Isaac-MindRobot-LeftArm-IK-Abs-v0",
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help="Name of the task (must be an Abs IK task).",
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)
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parser.add_argument(
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"--sensitivity", type=float, default=5.0, help="Sensitivity factor for pos/rot."
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"--sensitivity", type=float, default=None,
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help="Set both pos and rot sensitivity (overridden by --pos_sensitivity/--rot_sensitivity).",
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)
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parser.add_argument(
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"--arm",
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type=str,
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default="left",
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choices=["left", "right"],
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help="Which arm/controller to use.",
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)
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parser.add_argument(
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"--base_speed", type=float, default=3.0,
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help="Max wheel speed (rad/s) for joystick full forward.",
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)
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parser.add_argument(
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"--base_turn", type=float, default=2.0,
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help="Max wheel differential (rad/s) for joystick full left/right.",
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)
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# append AppLauncher cli args
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parser.add_argument("--pos_sensitivity", type=float, default=None, help="Position sensitivity. Default: 1.0.")
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parser.add_argument("--rot_sensitivity", type=float, default=None, help="Rotation sensitivity. Default: 0.3.")
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parser.add_argument("--arm", type=str, default="left", choices=["left", "right"], help="Which arm/controller to use.")
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parser.add_argument("--base_speed", type=float, default=3.0, help="Max wheel speed (rad/s) for joystick full forward.")
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parser.add_argument("--base_turn", type=float, default=2.0, help="Max wheel differential (rad/s) for joystick full left/right.")
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AppLauncher.add_app_launcher_args(parser)
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args_cli = parser.parse_args()
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app_launcher_args = vars(args_cli)
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# Disable some rendering settings to speed up
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app_launcher_args["xr"] = False
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# launch omniverse app
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app_launcher = AppLauncher(app_launcher_args)
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simulation_app = app_launcher.app
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@@ -83,7 +73,7 @@ from xr_utils.geometry import R_HEADSET_TO_WORLD
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# =====================================================================
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# Teleoperation Interface for XR
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# Helpers
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# =====================================================================
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def _quat_wxyz_to_rotation(quat_wxyz: np.ndarray) -> R:
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@@ -92,9 +82,9 @@ def _quat_wxyz_to_rotation(quat_wxyz: np.ndarray) -> R:
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def _rotation_to_quat_wxyz(rot: R) -> np.ndarray:
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"""Convert scipy Rotation quaternion to Isaac-style wxyz."""
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quat_xyzw = rot.as_quat()
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return np.array([quat_xyzw[3], quat_xyzw[0], quat_xyzw[1], quat_xyzw[2]])
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"""Convert scipy Rotation to Isaac-style wxyz quaternion."""
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q = rot.as_quat()
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return np.array([q[3], q[0], q[1], q[2]])
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def world_pose_to_root_frame(
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@@ -111,30 +101,39 @@ def world_pose_to_root_frame(
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return pos_root, quat_root
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class XrTeleopController:
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"""Teleop controller for PICO XR headset."""
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# =====================================================================
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# Teleoperation Controller
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# =====================================================================
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def __init__(self, arm="left", pos_sensitivity=1.0, rot_sensitivity=0.3, is_absolute=False):
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self.xr_client = XrClient()
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class XrTeleopController:
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"""Teleop controller for PICO XR headset (absolute IK mode).
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Accumulates an absolute EEF target in Isaac Sim world frame.
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Grip button acts as clutch; B/Y buttons trigger environment reset.
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"""
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def __init__(self, arm="left", pos_sensitivity=1.0, rot_sensitivity=0.3,
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max_pos_per_step=0.05, max_rot_per_step=0.08, xr_client=None):
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self.xr_client = xr_client if xr_client is not None else XrClient()
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self._owns_client = xr_client is None # only close if we created it
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self.pos_sensitivity = pos_sensitivity
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self.rot_sensitivity = rot_sensitivity
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# Hard caps per physics step to prevent IK divergence.
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# max_rot_per_step ~4.6°, max_pos_per_step 5 cm.
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self.max_pos_per_step = max_pos_per_step
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self.max_rot_per_step = max_rot_per_step
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self.arm = arm
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self.is_absolute = is_absolute
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self.controller_name = "left_controller" if arm == "left" else "right_controller"
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self.grip_name = "left_grip" if arm == "left" else "right_grip"
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self.trigger_name = "left_trigger" if arm == "left" else "right_trigger"
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# Coordinate transform matrix
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self.R_headset_world = R_HEADSET_TO_WORLD
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# Raw XR tracking space poses
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self.prev_xr_pos = None
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self.prev_xr_quat = None
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# Absolute target states
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self.target_eef_pos = None
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self.target_eef_quat = None # wxyz
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self.target_eef_pos = None # world frame
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self.target_eef_quat = None # world frame, wxyz
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self.grip_active = False
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self.frame_count = 0
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@@ -142,8 +141,6 @@ class XrTeleopController:
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self.require_grip_reengage = False
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self.grip_engage_threshold = 0.8
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self.grip_release_threshold = 0.2
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# Callbacks (like reset, etc)
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self.callbacks = {}
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def add_callback(self, name: str, func: Callable):
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@@ -156,40 +153,35 @@ class XrTeleopController:
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self.frame_count = 0
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self.target_eef_pos = None
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self.target_eef_quat = None
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# Require one grip release after reset so stale controller motion
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# cannot immediately drive the robot back toward the previous pose.
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# Require grip release after reset to avoid driving to stale pose.
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self.require_grip_reengage = True
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def close(self):
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self.xr_client.close()
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if self._owns_client:
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self.xr_client.close()
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def get_zero_action(self, trigger, current_eef_pos=None, current_eef_quat=None):
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if self.is_absolute:
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action = torch.zeros(8)
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# Stay at the current valid pose, or fallback to the cached target
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if current_eef_pos is not None and current_eef_quat is not None:
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action[:3] = torch.tensor(current_eef_pos.copy())
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action[3:7] = torch.tensor(current_eef_quat.copy())
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elif self.target_eef_pos is not None and self.target_eef_quat is not None:
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action[:3] = torch.tensor(self.target_eef_pos.copy())
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action[3:7] = torch.tensor(self.target_eef_quat.copy())
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else:
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action[3] = 1.0 # default w=1 for quat
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action[7] = 1.0 if trigger > 0.5 else -1.0
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def get_zero_action(self, trigger, current_eef_pos=None, current_eef_quat=None) -> torch.Tensor:
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"""Return 8D hold action [x, y, z, qw, qx, qy, qz, gripper] at frozen target pose."""
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action = torch.zeros(8)
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if self.target_eef_pos is not None and self.target_eef_quat is not None:
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# Prefer frozen world-frame target so IK holds a fixed world position
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# even if the robot chassis drifts slightly under gravity.
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action[:3] = torch.tensor(self.target_eef_pos.copy())
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action[3:7] = torch.tensor(self.target_eef_quat.copy())
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elif current_eef_pos is not None and current_eef_quat is not None:
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action[:3] = torch.tensor(current_eef_pos.copy())
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action[3:7] = torch.tensor(current_eef_quat.copy())
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else:
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action = torch.zeros(7)
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action[6] = 1.0 if trigger > 0.5 else -1.0
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action[3] = 1.0 # identity quaternion
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action[7] = 1.0 if trigger > 0.5 else -1.0
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return action
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def advance(self, current_eef_pos=None, current_eef_quat=None) -> torch.Tensor:
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"""Read XR controller and return 8D absolute action [x, y, z, qw, qx, qy, qz, gripper].
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Position and quaternion are in Isaac Sim world frame.
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Caller must convert to robot root frame before sending to IK.
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"""
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Reads the XR controller.
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Relative bounds return 7D action tensor: [dx, dy, dz, drx, dry, drz, gripper]
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Absolute bounds return 8D action tensor: [x, y, z, qw, qx, qy, qz, gripper]
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Note: in absolute mode current_eef_* and the returned target are in WORLD frame.
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The caller is responsible for converting to root frame before sending to IK.
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"""
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# XR buttons check (e.g. A or B for reset)
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try:
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reset_pressed = self.xr_client.get_button("B") or self.xr_client.get_button("Y")
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if reset_pressed and not self.reset_button_latched:
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@@ -203,141 +195,120 @@ class XrTeleopController:
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raw_pose = self.xr_client.get_pose(self.controller_name)
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grip = self.xr_client.get_key_value(self.grip_name)
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trigger = self.xr_client.get_key_value(self.trigger_name)
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except Exception as e:
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except Exception:
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return self.get_zero_action(0.0, current_eef_pos, current_eef_quat)
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# Skip transformation if quaternion is invalid
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if not is_valid_quaternion(raw_pose[3:]):
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return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
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# Wait for grip release after reset
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if self.require_grip_reengage:
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if grip <= self.grip_release_threshold:
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self.require_grip_reengage = False
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else:
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if self.is_absolute and current_eef_pos is not None and current_eef_quat is not None:
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if current_eef_pos is not None and current_eef_quat is not None:
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self.target_eef_pos = current_eef_pos.copy()
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self.target_eef_quat = current_eef_quat.copy()
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return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
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# Use hysteresis so noisy analog grip values do not accidentally re-enable teleop.
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if self.grip_active:
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grip_pressed = grip > self.grip_release_threshold
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else:
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grip_pressed = grip >= self.grip_engage_threshold
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# 握持键作为离合器 (Clutch)
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# Grip button as clutch with hysteresis
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grip_pressed = grip > self.grip_release_threshold if self.grip_active else grip >= self.grip_engage_threshold
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if not grip_pressed:
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self.prev_xr_pos = None
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self.prev_xr_quat = None
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||||
# Only snapshot target on the transition frame (grip just released) or first ever frame.
|
||||
# After that, keep it frozen so IK holds a fixed world-frame position.
|
||||
if self.grip_active or self.target_eef_pos is None:
|
||||
if 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()
|
||||
self.grip_active = False
|
||||
# Ensure target tracks the current pose while we are not grabbing
|
||||
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() # wxyz
|
||||
return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
|
||||
|
||||
if not self.grip_active:
|
||||
self.prev_xr_pos = raw_pose[:3].copy()
|
||||
self.prev_xr_quat = raw_pose[3:].copy()
|
||||
if self.is_absolute and current_eef_pos is not None and current_eef_quat is not None:
|
||||
if 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() # wxyz
|
||||
self.target_eef_quat = current_eef_quat.copy()
|
||||
self.grip_active = True
|
||||
return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
|
||||
|
||||
# Since OpenXR headset zero is not guaranteed to match robot zero, we compute the
|
||||
# raw transformation in World Frame, but apply it relatively to the stored target.
|
||||
|
||||
# 1. First, find current XR pose projected into World frame
|
||||
# Project current and previous XR poses into Isaac Sim world frame
|
||||
xr_world_pos, xr_world_quat_xyzw = transform_xr_pose(raw_pose[:3], raw_pose[3:])
|
||||
prev_xr_world_pos, prev_xr_world_quat_xyzw = transform_xr_pose(self.prev_xr_pos, self.prev_xr_quat)
|
||||
|
||||
# 2. Extract Delta POS in World frame
|
||||
# Delta position (world frame), clamped per step
|
||||
world_delta_pos = (xr_world_pos - prev_xr_world_pos) * self.pos_sensitivity
|
||||
pos_norm = np.linalg.norm(world_delta_pos)
|
||||
if pos_norm > self.max_pos_per_step:
|
||||
world_delta_pos *= self.max_pos_per_step / pos_norm
|
||||
|
||||
# 3. Extract Delta ROT in World frame
|
||||
# Delta rotation (world frame), clamped per step
|
||||
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(world_delta_rot)
|
||||
if rot_norm > self.max_rot_per_step:
|
||||
world_delta_rot *= self.max_rot_per_step / rot_norm
|
||||
|
||||
# 4. Gripper
|
||||
gripper_action = 1.0 if trigger > 0.5 else -1.0
|
||||
|
||||
if self.is_absolute:
|
||||
if self.target_eef_pos is None:
|
||||
self.target_eef_pos = np.zeros(3)
|
||||
self.target_eef_quat = np.array([1.0, 0.0, 0.0, 0.0])
|
||||
# Accumulate absolute target
|
||||
if self.target_eef_pos is None:
|
||||
self.target_eef_pos = np.zeros(3)
|
||||
self.target_eef_quat = np.array([1.0, 0.0, 0.0, 0.0])
|
||||
|
||||
# Accumulate in world frame so VR direction always matches sim direction.
|
||||
self.target_eef_pos += world_delta_pos
|
||||
self.target_eef_pos += world_delta_pos
|
||||
|
||||
target_r = _quat_wxyz_to_rotation(self.target_eef_quat)
|
||||
delta_r = R.from_rotvec(world_delta_rot)
|
||||
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],
|
||||
self.target_eef_quat[0], self.target_eef_quat[1], self.target_eef_quat[2], self.target_eef_quat[3],
|
||||
gripper_action], dtype=torch.float32)
|
||||
|
||||
self.prev_xr_pos = raw_pose[:3].copy()
|
||||
self.prev_xr_quat = raw_pose[3:].copy()
|
||||
|
||||
else:
|
||||
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 = 0.15
|
||||
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],
|
||||
world_delta_rot[0], world_delta_rot[1], world_delta_rot[2],
|
||||
gripper_action], dtype=torch.float32)
|
||||
|
||||
self.prev_xr_pos = raw_pose[:3].copy()
|
||||
self.prev_xr_quat = raw_pose[3:].copy()
|
||||
# Apply rotation delta in world frame so controller direction = EEF world direction
|
||||
target_r = _quat_wxyz_to_rotation(self.target_eef_quat)
|
||||
self.target_eef_quat = _rotation_to_quat_wxyz(R.from_rotvec(world_delta_rot) * target_r)
|
||||
|
||||
self.prev_xr_pos = raw_pose[:3].copy()
|
||||
self.prev_xr_quat = raw_pose[3:].copy()
|
||||
self.frame_count += 1
|
||||
|
||||
action = torch.tensor([
|
||||
*self.target_eef_pos,
|
||||
*self.target_eef_quat,
|
||||
gripper_action,
|
||||
], dtype=torch.float32)
|
||||
|
||||
if self.frame_count % 30 == 0:
|
||||
np.set_printoptions(precision=4, suppress=True, floatmode='fixed')
|
||||
print("\n====================== [VR TELEOP DEBUG] ======================")
|
||||
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={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 (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()}")
|
||||
print(f"| Targ Pos (world): {action[:3].numpy()}")
|
||||
print(f"| Targ Quat (world, wxyz): {action[3:7].numpy()}")
|
||||
print(f"| Gripper & Trigger: Grip={grip:.2f}, Trig={trigger:.2f}")
|
||||
print("===============================================================")
|
||||
|
||||
return action
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Main Execution Loop
|
||||
# =====================================================================
|
||||
|
||||
def main() -> None:
|
||||
"""Run teleoperation with PICO XR Controller against Isaac Lab environment."""
|
||||
|
||||
# 1. Configuration parsing
|
||||
|
||||
env_cfg = parse_env_cfg(args_cli.task, num_envs=args_cli.num_envs)
|
||||
env_cfg.env_name = args_cli.task
|
||||
|
||||
if not isinstance(env_cfg, ManagerBasedRLEnvCfg):
|
||||
raise ValueError(f"Teleoperation requires ManagerBasedRLEnvCfg. Got: {type(env_cfg)}")
|
||||
if "Abs" not in args_cli.task:
|
||||
raise ValueError(
|
||||
f"Task '{args_cli.task}' is not an absolute IK task. "
|
||||
"Only Abs tasks are supported (e.g. Isaac-MindRobot-LeftArm-IK-Abs-v0)."
|
||||
)
|
||||
|
||||
env_cfg.terminations.time_out = None
|
||||
|
||||
# 2. Environment creation
|
||||
try:
|
||||
env = gym.make(args_cli.task, cfg=env_cfg).unwrapped
|
||||
except Exception as e:
|
||||
@@ -345,19 +316,41 @@ def main() -> None:
|
||||
simulation_app.close()
|
||||
return
|
||||
|
||||
# 3. Teleoperation Interface Initialization
|
||||
is_abs_mode = "Abs" in args_cli.task
|
||||
|
||||
print(f"\n[INFO] Connecting to PICO XR Headset using {args_cli.arm} controller...")
|
||||
print(f"[INFO] Using IK Mode: {'ABSOLUTE' if is_abs_mode else 'RELATIVE'}")
|
||||
teleop_interface = XrTeleopController(
|
||||
arm=args_cli.arm,
|
||||
pos_sensitivity=args_cli.sensitivity,
|
||||
rot_sensitivity=args_cli.sensitivity * (1.0 if is_abs_mode else 0.3), # Absolute rotation handles 1:1 better than relative
|
||||
is_absolute=is_abs_mode
|
||||
)
|
||||
|
||||
# Detect single-arm vs dual-arm based on action manager term names
|
||||
is_dual_arm = "left_arm_action" in env.action_manager._terms
|
||||
|
||||
# Sensitivity: explicit args override --sensitivity, which overrides defaults
|
||||
pos_sens = 1.0
|
||||
rot_sens = 0.3
|
||||
if args_cli.sensitivity is not None:
|
||||
pos_sens = args_cli.sensitivity
|
||||
rot_sens = args_cli.sensitivity
|
||||
if args_cli.pos_sensitivity is not None:
|
||||
pos_sens = args_cli.pos_sensitivity
|
||||
if args_cli.rot_sensitivity is not None:
|
||||
rot_sens = args_cli.rot_sensitivity
|
||||
|
||||
if is_dual_arm:
|
||||
print(f"\n[INFO] Dual-arm mode detected. Using both controllers.")
|
||||
print(f"[INFO] IK Mode: ABSOLUTE")
|
||||
print(f"[INFO] Sensitivity: pos={pos_sens:.3f} rot={rot_sens:.3f}")
|
||||
shared_client = XrClient()
|
||||
teleop_left = XrTeleopController(arm="left", pos_sensitivity=pos_sens, rot_sensitivity=rot_sens, xr_client=shared_client)
|
||||
teleop_right = XrTeleopController(arm="right", pos_sensitivity=pos_sens, rot_sensitivity=rot_sens, xr_client=shared_client)
|
||||
teleop_interface = teleop_left # primary interface for callbacks/reset
|
||||
teleop_right_ref = teleop_right
|
||||
else:
|
||||
print(f"\n[INFO] Connecting to PICO XR Headset using {args_cli.arm} controller...")
|
||||
print(f"[INFO] IK Mode: ABSOLUTE")
|
||||
print(f"[INFO] Sensitivity: pos={pos_sens:.3f} rot={rot_sens:.3f}")
|
||||
teleop_interface = XrTeleopController(
|
||||
arm=args_cli.arm,
|
||||
pos_sensitivity=pos_sens,
|
||||
rot_sensitivity=rot_sens,
|
||||
)
|
||||
|
||||
should_reset = False
|
||||
|
||||
def request_reset():
|
||||
nonlocal should_reset
|
||||
should_reset = True
|
||||
@@ -365,30 +358,34 @@ 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
|
||||
"""Sync IK controller's internal target to current EEF pose to avoid jumps after reset."""
|
||||
if is_dual_arm:
|
||||
for term_key in ["left_arm_action", "right_arm_action"]:
|
||||
arm_action_term = env.action_manager._terms[term_key]
|
||||
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
|
||||
else:
|
||||
arm_action_term = env.action_manager._terms["arm_action"]
|
||||
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."""
|
||||
"""Convert 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()
|
||||
root_quat_w = robot.data.root_quat_w[0].detach().cpu().numpy()
|
||||
pos_root, quat_root = world_pose_to_root_frame(
|
||||
target_pos_w, target_quat_w, root_pos_w, root_quat_w,
|
||||
action_tensor[:3].numpy(), action_tensor[3:7].numpy(),
|
||||
root_pos_w, root_quat_w,
|
||||
)
|
||||
out = action_tensor.clone()
|
||||
out[:3] = torch.tensor(pos_root, dtype=torch.float32)
|
||||
@@ -396,53 +393,43 @@ def main() -> None:
|
||||
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.
|
||||
"""
|
||||
"""Read left joystick → 4-DOF wheel velocity [right_b, left_b, left_f, right_f]."""
|
||||
try:
|
||||
joy = teleop_interface.xr_client.get_joystick("left")
|
||||
jy = float(joy[1]) # forward / backward
|
||||
jx = float(joy[0]) # right / left
|
||||
jy = float(joy[1])
|
||||
jx = float(joy[0])
|
||||
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)
|
||||
|
||||
return torch.tensor(
|
||||
[right_vel, left_vel, left_vel, right_vel], dtype=torch.float32
|
||||
)
|
||||
|
||||
env.reset()
|
||||
obs, _ = env.reset()
|
||||
clear_ik_target_state()
|
||||
teleop_interface.reset()
|
||||
if is_dual_arm:
|
||||
teleop_right_ref.reset()
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print(" 🚀 Teleoperation Started!")
|
||||
print(" 🎮 Use the TRIGGER to open/close gripper.")
|
||||
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(" Teleoperation Started!")
|
||||
if is_dual_arm:
|
||||
print(" LEFT controller → left arm")
|
||||
print(" RIGHT controller → right arm")
|
||||
print(" TRIGGER: open/close gripper")
|
||||
print(" GRIP (hold): move the arm")
|
||||
print(" Left joystick: Y=forward/back, X=turn")
|
||||
print(" B / Y: reset environment")
|
||||
print("=" * 50 + "\n")
|
||||
|
||||
device = env.unwrapped.device
|
||||
sim_frame = 0
|
||||
obs, _ = env.reset()
|
||||
clear_ik_target_state()
|
||||
|
||||
last_root_left = None
|
||||
last_root_right = None
|
||||
|
||||
while simulation_app.is_running():
|
||||
try:
|
||||
with torch.inference_mode():
|
||||
@@ -450,66 +437,78 @@ def main() -> None:
|
||||
obs, _ = env.reset()
|
||||
clear_ik_target_state()
|
||||
teleop_interface.reset()
|
||||
if is_dual_arm:
|
||||
teleop_right_ref.reset()
|
||||
should_reset = False
|
||||
sim_frame = 0
|
||||
last_root_left = None
|
||||
last_root_right = None
|
||||
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 (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:]])
|
||||
|
||||
if is_dual_arm:
|
||||
eef_pos_left = policy_obs["eef_pos_left"][0].cpu().numpy()
|
||||
eef_quat_left = policy_obs["eef_quat_left"][0].cpu().numpy()
|
||||
eef_pos_right = policy_obs["eef_pos_right"][0].cpu().numpy()
|
||||
eef_quat_right = policy_obs["eef_quat_right"][0].cpu().numpy()
|
||||
|
||||
left_action = teleop_left.advance(current_eef_pos=eef_pos_left, current_eef_quat=eef_quat_left)
|
||||
right_action = teleop_right_ref.advance(current_eef_pos=eef_pos_right, current_eef_quat=eef_quat_right)
|
||||
|
||||
# Only recompute root-frame IK target when grip is active; otherwise freeze joints
|
||||
if teleop_left.grip_active or last_root_left is None:
|
||||
last_root_left = convert_action_world_to_root(left_action)[:7].clone()
|
||||
if teleop_right_ref.grip_active or last_root_right is None:
|
||||
last_root_right = convert_action_world_to_root(right_action)[:7].clone()
|
||||
|
||||
# Action layout: left_arm(7) | wheel(4) | left_gripper(1) | right_arm(7) | right_gripper(1)
|
||||
action_np = torch.cat([
|
||||
last_root_left, wheel_np, left_action[7:8],
|
||||
last_root_right, right_action[7:8],
|
||||
])
|
||||
else:
|
||||
eef_pos = policy_obs["eef_pos"][0].cpu().numpy()
|
||||
eef_quat = policy_obs["eef_quat"][0].cpu().numpy()
|
||||
|
||||
action_np = teleop_interface.advance(current_eef_pos=eef_pos, current_eef_quat=eef_quat)
|
||||
|
||||
if teleop_interface.grip_active or last_root_left is None:
|
||||
last_root_left = convert_action_world_to_root(action_np)[:7].clone()
|
||||
|
||||
# Action layout: arm(7) | wheel(4) | gripper(1)
|
||||
action_np = torch.cat([last_root_left, wheel_np, action_np[7:8]])
|
||||
|
||||
actions = action_np.unsqueeze(0).repeat(env.num_envs, 1).to(device)
|
||||
|
||||
# Step environment
|
||||
obs, _, _, _, _ = env.step(actions)
|
||||
|
||||
# Print robot state every 30 frames
|
||||
sim_frame += 1
|
||||
if sim_frame % 30 == 0:
|
||||
np.set_printoptions(precision=4, suppress=True, floatmode='fixed')
|
||||
policy_obs = obs["policy"]
|
||||
joint_pos = policy_obs["joint_pos"][0].cpu().numpy()
|
||||
eef_pos = policy_obs["eef_pos"][0].cpu().numpy()
|
||||
eef_quat = policy_obs["eef_quat"][0].cpu().numpy()
|
||||
last_act = policy_obs["actions"][0].cpu().numpy()
|
||||
|
||||
# On first print, dump ALL joint names + positions to identify indices
|
||||
if sim_frame == 30:
|
||||
robot = env.unwrapped.scene["robot"]
|
||||
jnames = robot.joint_names
|
||||
print(f"\n{'='*70}")
|
||||
print(f" ALL {len(jnames)} JOINT NAMES AND POSITIONS (relative)")
|
||||
print(f" ALL {len(jnames)} JOINT NAMES AND POSITIONS")
|
||||
print(f"{'='*70}")
|
||||
for i, name in enumerate(jnames):
|
||||
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 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 n.startswith("l_joint")]
|
||||
arm_idx = env._arm_idx_cache
|
||||
arm_joints = joint_pos[arm_idx]
|
||||
env._right_arm_idx_cache = [i for i, n in enumerate(jnames) if n.startswith("r_joint")]
|
||||
arm_joints = joint_pos[env._arm_idx_cache]
|
||||
right_arm_joints = joint_pos[env._right_arm_idx_cache]
|
||||
|
||||
try:
|
||||
joy_dbg = teleop_interface.xr_client.get_joystick("left")
|
||||
@@ -517,21 +516,59 @@ def main() -> None:
|
||||
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 is_dual_arm:
|
||||
eef_pos_left = policy_obs["eef_pos_left"][0].cpu().numpy()
|
||||
eef_quat_left = policy_obs["eef_quat_left"][0].cpu().numpy()
|
||||
eef_pos_right = policy_obs["eef_pos_right"][0].cpu().numpy()
|
||||
print(f"\n---------------- [ROBOT STATE frame={sim_frame}] ----------------")
|
||||
print(f"| Left Arm Joints (rad): {arm_joints}")
|
||||
print(f"| Right Arm Joints (rad): {right_arm_joints}")
|
||||
print(f"| Left EEF Pos (world, m): {eef_pos_left}")
|
||||
print(f"| Right EEF Pos (world, m): {eef_pos_right}")
|
||||
print(f"| Cmd left_arm(abs): {last_act[:7]}")
|
||||
print(f"| Cmd wheel vel (rad/s): {last_act[7:11]}")
|
||||
print(f"| Cmd left_gripper: {last_act[11:12]} (+1=open -1=close)")
|
||||
print(f"| Cmd right_arm(abs): {last_act[12:19]}")
|
||||
print(f"| Cmd right_gripper: {last_act[19:]} (+1=open -1=close)")
|
||||
print(f"| Joystick (x=turn,y=fwd): {joy_str}")
|
||||
print(f"----------------------------------------------------------------")
|
||||
else:
|
||||
eef_pos = policy_obs["eef_pos"][0].cpu().numpy()
|
||||
eef_quat = policy_obs["eef_quat"][0].cpu().numpy()
|
||||
|
||||
if not hasattr(env, '_prev_eef_quat'):
|
||||
env._prev_eef_quat = eef_quat.copy()
|
||||
prev_q = env._prev_eef_quat
|
||||
r_prev = R.from_quat([prev_q[1], prev_q[2], prev_q[3], prev_q[0]])
|
||||
r_curr = R.from_quat([eef_quat[1], eef_quat[2], eef_quat[3], eef_quat[0]])
|
||||
delta_rotvec = (r_curr * r_prev.inv()).as_rotvec()
|
||||
delta_angle_deg = np.degrees(np.linalg.norm(delta_rotvec))
|
||||
delta_axis = delta_rotvec / (np.linalg.norm(delta_rotvec) + 1e-9)
|
||||
env._prev_eef_quat = eef_quat.copy()
|
||||
approach_vec = r_curr.apply(np.array([1.0, 0.0, 0.0]))
|
||||
|
||||
print(f"\n---------------- [ROBOT STATE frame={sim_frame}] ----------------")
|
||||
print(f"| Left Arm Joints (rad): {arm_joints}")
|
||||
print(f"| EEF Pos (world, m): {eef_pos}")
|
||||
print(f"| EEF approach vec (world): [{approach_vec[0]:+.3f}, {approach_vec[1]:+.3f}, {approach_vec[2]:+.3f}] (夹爪朝向)")
|
||||
print(f"| EEF rot since last print: {delta_angle_deg:+.2f}° around [{delta_axis[0]:+.3f},{delta_axis[1]:+.3f},{delta_axis[2]:+.3f}] (world)")
|
||||
print(f"| Cmd arm(abs pos+quat): {last_act[:7]}")
|
||||
print(f"| Cmd wheel vel (rad/s): {last_act[7:11]}")
|
||||
print(f"| Cmd gripper: {last_act[11:]} (+1=open -1=close)")
|
||||
print(f"| Joystick (x=turn,y=fwd): {joy_str}")
|
||||
print(f"----------------------------------------------------------------")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during simulation step: {e}")
|
||||
break
|
||||
|
||||
teleop_interface.close()
|
||||
if is_dual_arm:
|
||||
teleop_right_ref.close()
|
||||
shared_client.close()
|
||||
env.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
simulation_app.close()
|
||||
|
||||
Reference in New Issue
Block a user