538 lines
22 KiB
Python
538 lines
22 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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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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"""
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import argparse
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import logging
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import sys
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import os
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from collections.abc import Callable
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# Ensure xr_utils (next to this script) is importable when running directly
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from isaaclab.app import AppLauncher
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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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)
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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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)
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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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)
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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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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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"""Rest everything follows."""
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import gymnasium as gym
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import numpy as np
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import torch
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from scipy.spatial.transform import Rotation as R
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from isaaclab.envs import ManagerBasedRLEnvCfg
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import isaaclab_tasks # noqa: F401
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import mindbot.tasks # noqa: F401
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from isaaclab_tasks.utils import parse_env_cfg
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from xr_utils import XrClient, transform_xr_pose, quat_diff_as_rotvec_xyzw, is_valid_quaternion
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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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# =====================================================================
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def _quat_wxyz_to_rotation(quat_wxyz: np.ndarray) -> R:
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"""Convert Isaac-style wxyz quaternion to scipy Rotation."""
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return R.from_quat([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]])
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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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def world_pose_to_root_frame(
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pos_w: np.ndarray,
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quat_wxyz: np.ndarray,
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root_pos_w: np.ndarray,
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root_quat_wxyz: np.ndarray,
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) -> tuple[np.ndarray, np.ndarray]:
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"""Express a world-frame pose in the robot root frame."""
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root_rot = _quat_wxyz_to_rotation(root_quat_wxyz)
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pose_rot = _quat_wxyz_to_rotation(quat_wxyz)
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pos_root = root_rot.inv().apply(pos_w - root_pos_w)
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quat_root = _rotation_to_quat_wxyz(root_rot.inv() * pose_rot)
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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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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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self.pos_sensitivity = pos_sensitivity
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self.rot_sensitivity = rot_sensitivity
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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.grip_active = False
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self.frame_count = 0
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self.reset_button_latched = False
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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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self.callbacks[name] = func
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def reset(self):
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self.prev_xr_pos = None
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self.prev_xr_quat = None
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self.grip_active = False
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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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self.require_grip_reengage = True
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def close(self):
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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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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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return action
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def advance(self, current_eef_pos=None, current_eef_quat=None) -> torch.Tensor:
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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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if "RESET" in self.callbacks:
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self.callbacks["RESET"]()
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self.reset_button_latched = reset_pressed
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except Exception:
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pass
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try:
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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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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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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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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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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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self.grip_active = False
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# Ensure target tracks the current pose while we are not grabbing
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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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self.target_eef_pos = current_eef_pos.copy()
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self.target_eef_quat = current_eef_quat.copy() # wxyz
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return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
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if not self.grip_active:
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self.prev_xr_pos = raw_pose[:3].copy()
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self.prev_xr_quat = raw_pose[3:].copy()
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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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self.target_eef_pos = current_eef_pos.copy()
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self.target_eef_quat = current_eef_quat.copy() # wxyz
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self.grip_active = True
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return self.get_zero_action(trigger, current_eef_pos, current_eef_quat)
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# Since OpenXR headset zero is not guaranteed to match robot zero, we compute the
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# raw transformation in World Frame, but apply it relatively to the stored target.
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# 1. First, find current XR pose projected into World frame
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xr_world_pos, xr_world_quat_xyzw = transform_xr_pose(raw_pose[:3], raw_pose[3:])
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prev_xr_world_pos, prev_xr_world_quat_xyzw = transform_xr_pose(self.prev_xr_pos, self.prev_xr_quat)
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# 2. Extract Delta POS in World frame
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world_delta_pos = (xr_world_pos - prev_xr_world_pos) * self.pos_sensitivity
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# 3. Extract Delta ROT in World frame
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world_delta_rot = quat_diff_as_rotvec_xyzw(prev_xr_world_quat_xyzw, xr_world_quat_xyzw) * self.rot_sensitivity
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# 4. Gripper
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gripper_action = 1.0 if trigger > 0.5 else -1.0
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if self.is_absolute:
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if self.target_eef_pos is None:
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self.target_eef_pos = np.zeros(3)
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self.target_eef_quat = np.array([1.0, 0.0, 0.0, 0.0])
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# Accumulate in world frame so VR direction always matches sim direction.
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self.target_eef_pos += world_delta_pos
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target_r = _quat_wxyz_to_rotation(self.target_eef_quat)
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delta_r = R.from_rotvec(world_delta_rot)
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self.target_eef_quat = _rotation_to_quat_wxyz(delta_r * target_r)
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action = torch.tensor([
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self.target_eef_pos[0], self.target_eef_pos[1], self.target_eef_pos[2],
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self.target_eef_quat[0], self.target_eef_quat[1], self.target_eef_quat[2], self.target_eef_quat[3],
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gripper_action], dtype=torch.float32)
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self.prev_xr_pos = raw_pose[:3].copy()
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self.prev_xr_quat = raw_pose[3:].copy()
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else:
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max_pos_delta = 0.05
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world_pos_norm = np.linalg.norm(world_delta_pos)
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if world_pos_norm > max_pos_delta:
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world_delta_pos = world_delta_pos * (max_pos_delta / world_pos_norm)
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max_rot_delta = 0.15
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world_rot_norm = np.linalg.norm(world_delta_rot)
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if world_rot_norm > max_rot_delta:
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world_delta_rot = world_delta_rot * (max_rot_delta / world_rot_norm)
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action = torch.tensor([
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world_delta_pos[0], world_delta_pos[1], world_delta_pos[2],
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world_delta_rot[0], world_delta_rot[1], world_delta_rot[2],
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gripper_action], dtype=torch.float32)
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self.prev_xr_pos = raw_pose[:3].copy()
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self.prev_xr_quat = raw_pose[3:].copy()
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self.frame_count += 1
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if self.frame_count % 30 == 0:
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np.set_printoptions(precision=4, suppress=True, floatmode='fixed')
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print("\n====================== [VR TELEOP DEBUG] ======================")
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print(f"| Task Mode: {'ABSOLUTE' if self.is_absolute else 'RELATIVE'}")
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print(f"| Raw VR Pos (OpenXR): {np.array(raw_pose[:3])}")
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print(f"| Raw VR Quat (xyzw): {np.array(raw_pose[3:])}")
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print(f"| XR Delta Pos (world): {world_delta_pos} (norm={np.linalg.norm(world_delta_pos):.4f})")
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print(f"| XR Delta Rot (world): {world_delta_rot} (norm={np.linalg.norm(world_delta_rot):.4f})")
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if self.is_absolute:
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print(f"| Targ Pos (world): {action[:3].numpy()}")
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print(f"| Targ Quat (world, wxyz): {action[3:7].numpy()}")
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else:
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print(f"| Sent Action Pos (dx,dy,dz): {action[:3].numpy()}")
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print(f"| Sent Action Rot (rx,ry,rz): {action[3:6].numpy()}")
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print(f"| Gripper & Trigger: Grip={grip:.2f}, Trig={trigger:.2f}")
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print("===============================================================")
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return action
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# =====================================================================
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# Main Execution Loop
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# =====================================================================
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def main() -> None:
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"""Run teleoperation with PICO XR Controller against Isaac Lab environment."""
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# 1. Configuration parsing
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env_cfg = parse_env_cfg(args_cli.task, num_envs=args_cli.num_envs)
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env_cfg.env_name = args_cli.task
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if not isinstance(env_cfg, ManagerBasedRLEnvCfg):
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raise ValueError(f"Teleoperation requires ManagerBasedRLEnvCfg. Got: {type(env_cfg)}")
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env_cfg.terminations.time_out = None
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# 2. Environment creation
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try:
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env = gym.make(args_cli.task, cfg=env_cfg).unwrapped
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except Exception as e:
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logger.error(f"Failed to create environment '{args_cli.task}': {e}")
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simulation_app.close()
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return
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# 3. Teleoperation Interface Initialization
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is_abs_mode = "Abs" in args_cli.task
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print(f"\n[INFO] Connecting to PICO XR Headset using {args_cli.arm} controller...")
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print(f"[INFO] Using IK Mode: {'ABSOLUTE' if is_abs_mode else 'RELATIVE'}")
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teleop_interface = XrTeleopController(
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arm=args_cli.arm,
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pos_sensitivity=args_cli.sensitivity,
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rot_sensitivity=args_cli.sensitivity * (1.0 if is_abs_mode else 0.3), # Absolute rotation handles 1:1 better than relative
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is_absolute=is_abs_mode
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)
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should_reset = False
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def request_reset():
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nonlocal should_reset
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should_reset = True
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print("[INFO] Reset requested via XR button.")
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teleop_interface.add_callback("RESET", request_reset)
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def get_arm_action_term():
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return env.action_manager._terms["arm_action"]
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def clear_ik_target_state():
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"""Clear the internal IK target so reset does not reuse the previous pose command."""
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if not is_abs_mode:
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return
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arm_action_term = get_arm_action_term()
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ee_pos_b, ee_quat_b = arm_action_term._compute_frame_pose()
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arm_action_term._raw_actions.zero_()
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arm_action_term._processed_actions.zero_()
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arm_action_term._ik_controller._command.zero_()
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arm_action_term._ik_controller.ee_pos_des[:] = ee_pos_b
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arm_action_term._ik_controller.ee_quat_des[:] = ee_quat_b
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def convert_action_world_to_root(action_tensor: torch.Tensor) -> torch.Tensor:
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"""Convert an absolute IK action from world frame to robot root frame."""
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robot = env.unwrapped.scene["robot"]
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root_pos_w = robot.data.root_pos_w[0].detach().cpu().numpy()
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root_quat_w = robot.data.root_quat_w[0].detach().cpu().numpy() # wxyz
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target_pos_w = action_tensor[:3].numpy()
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target_quat_w = action_tensor[3:7].numpy()
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pos_root, quat_root = world_pose_to_root_frame(
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target_pos_w, target_quat_w, root_pos_w, root_quat_w,
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)
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out = action_tensor.clone()
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out[:3] = torch.tensor(pos_root, dtype=torch.float32)
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out[3:7] = torch.tensor(quat_root, dtype=torch.float32)
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return out
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def get_wheel_action() -> torch.Tensor:
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"""Read left joystick and return 4-DOF wheel velocity command.
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Skid-steer differential drive.
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Joystick: Y-axis (+1 = forward), X-axis (+1 = right turn).
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Joint order from articulation (terminal log):
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[right_b, left_b, left_f, right_f]
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Right/left sign convention assumes both sides' joints have the same
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axis direction (positive velocity = forward). If the robot drives
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backward when pushing forward, negate base_speed in the launch command.
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"""
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try:
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joy = teleop_interface.xr_client.get_joystick("left")
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jy = float(joy[1]) # forward / backward
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jx = float(joy[0]) # right / left
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except Exception:
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return torch.zeros(4)
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v = jy * args_cli.base_speed
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omega = jx * args_cli.base_turn
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# Positive omega = turn right → left wheels faster, right wheels slower
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right_vel = v - omega
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left_vel = v + omega
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return torch.tensor(
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[right_vel, left_vel, left_vel, right_vel], dtype=torch.float32
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)
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env.reset()
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clear_ik_target_state()
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teleop_interface.reset()
|
|
|
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print("\n" + "=" * 50)
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print(" 🚀 Teleoperation Started!")
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|
print(" 🎮 Use the TRIGGER to open/close gripper.")
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|
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():
|
|
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 (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
|
|
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"{'='*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]
|
|
|
|
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"----------------------------------------------------------------")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error during simulation step: {e}")
|
|
break
|
|
|
|
teleop_interface.close()
|
|
env.close()
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
simulation_app.close()
|