Whole-body teleoperation is essential for scalable robot data collection in loco-manipulation tasks, yet existing approaches relying on exoskeleton suits or multi-camera setups impose prohibitive cost, complexity, and environmental constraints. Recent methods using a single extended reality (XR) device with end-to-end reinforcement learning policies partially address these limitations but require robot-specific retraining, suffer from out-of-distribution failures, and rely on motion retargeting that neglects dynamic feasibility. We propose a hierarchical whole-body teleoperation framework driven by a single XR device that generalizes across diverse robot morphologies without retraining robot-specific policies. A Model Predictive Control (MPC)-based motion retargeter jointly optimizes alignment with the operator's intent and the robot's dynamic feasibility, generating optimal commands for existing low-level controllers. To ensure robust online execution, we introduce a state synchronization method that resets the simulator state at each MPC step to handle noisy real-world measurements and contact sensitivity, and integrate SLAM-based global pose feedback to mitigate long-term drift. Simulation results show higher success rates on whole-body control tasks for both a humanoid (over 30% lower completion time and 20% lower power consumption) and a mobile manipulator (zero collisions) compared to baselines. Real-world experiments further validate the effectiveness and flexibility of our method, demonstrating the successful deployment of the proposed retargeter on both platforms for whole-body control tasks and the ease of allowing users to adjust teleoperation behavior based on their preferences. This plug-and-play framework offers a scalable, morphology-agnostic solution for whole-body robot teleoperation, enabling real-time behavioral customization and broad applicability across platforms.

翻译：全身遥操作对于可扩展的机器人数据采集在半具身操纵任务中至关重要，然而现有依赖外骨骼服或多摄像头系统的方法存在成本高昂、复杂度高且受环境限制等问题。近期采用单一扩展现实（XR）设备结合端到端强化学习策略的研究虽部分解决了上述限制，但需针对特定机器人重新训练，存在分布外失败案例，且依赖忽略动态可行性的运动重定向方法。本文提出一种由单一XR设备驱动的分层全身遥操作框架，无需针对特定机器人重新训练即可泛化至不同机器人形态。基于模型预测控制（MPC）的运动重定向器通过联合优化操作者意图与机器人动态可行性，为现有底层控制器生成最优指令。为确保在线执行的鲁棒性，我们提出状态同步方法，在每个MPC步骤重置仿真器状态以应对含噪真实世界测量值与接触敏感性，并集成基于SLAM的全局位姿反馈以缓解长期漂移。仿真实验表明，在类人机器人（完成时间降低超30%、能耗降低20%）与移动操作臂（零碰撞）的全身控制任务中，该方法相较基线取得更高成功率。真实世界实验进一步验证了该方法的有效性与灵活性，证明了所提出重定向器在两个平台全身控制任务中的成功部署，以及用户可根据偏好轻松调整遥操作行为的实用性。该即插即用框架为全身机器人遥操作提供了一种可扩展、形态无关的解决方案，可实现实时行为定制并广泛适用于多种平台。