CLONE系统实现闭环控制，让机器人长时间保持精准动作协调。
CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks
- 基于MoE架构，通过头手追踪实现全身闭环控制。
- 长距离移动中位置漂移极小，支持连续拾取等复杂动作。
- 适合需要长时间稳定操作的机器人场景应用。
人形机器人遥操作在展示和收集复杂人机交互数据方面至关重要。然而，现有系统将上肢与下肢控制解耦以维持稳定性，限制了自然协调性，并采用开环模式缺乏实时位置反馈，导致累积漂移。根本挑战在于如何在长时间内实现精确且协调的全身遥操作并保持准确全局定位。本文提出基于混合专家（MoE）架构的闭路遥操作系统CLONE，通过实时误差校正实现了前所未有的全身遥操作保真度，在仅使用增强现实头显的头部与手部追踪信号的情况下，长期轨迹中保持最小位置漂移。相较于以往方法或牺牲协调性换取稳定、或因无界漂移而失效，CLONE能学习多样运动技能，通过实时反馈防止追踪误差积累，实现如“从地面拾取物体”等复杂协调动作。该成果为长时程人形-场景交互任务树立了新里程碑。
原文摘要 · Abstract (English)
Humanoid teleoperation plays a vital role in demonstrating and collecting data for complex humanoid-scene interactions. However, current teleoperation systems face critical limitations: they decouple upper- and lower-body control to maintain stability, restricting natural coordination, and operate open-loop without real-time position feedback, leading to accumulated drift. The fundamental challenge is achieving precise, coordinated whole-body teleoperation over extended durations while maintaining accurate global positioning. Here we show that an MoE-based teleoperation system, CLONE, with closed-loop error correction enables unprecedented whole-body teleoperation fidelity, maintaining minimal positional drift over long-range trajectories using only head and hand tracking from an MR headset. Unlike previous methods that either sacrifice coordination for stability or suffer from unbounded drift, CLONE learns diverse motion skills while preventing tracking error accumulation through real-time feedback, enabling complex coordinated movements such as ``picking up objects from the ground.'' These results establish a new milestone for whole-body humanoid teleoperation for long-horizon humanoid-scene interaction tasks.
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