让机器人操作员像司机握方向盘一样实时干预，提升学习效率。
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning
- 人机双向同步控制，操作反馈如方向盘般自然
- 实测提升模仿学习数据效率，增强机器人纠错能力
- 低成本3D打印结构，适合科研与教学场景
遥操作对自主机器人学习至关重要，尤其在需人类示范或修正的抓取任务中。然而，现有系统多为单向控制，无法实时同步机器人状态与操作设备，难以灵活干预。本文提出HACTS（Human-As-Copilot Teleoperation System），建立机械臂与遥操作设备间的双向实时关节同步。该机制类似自动驾驶中的方向盘，使操作员可无缝介入，并同步收集动作-修正数据用于后续学习。系统采用3D打印部件和低成本现成电机实现，兼具可及性与可扩展性。实验表明，HACTS显著提升模仿学习（IL）与强化学习（RL）性能，增强IL恢复能力与数据效率，支持人机协同强化学习。该系统推动更高效、互动性强的人机协作与数据采集，助力机器人操作能力发展。
原文摘要 · Abstract (English)
Teleoperation is essential for autonomous robot learning, especially in manipulation tasks that require human demonstrations or corrections. However, most existing systems only offer unilateral robot control and lack the ability to synchronize the robot's status with the teleoperation hardware, preventing real-time, flexible intervention. In this work, we introduce HACTS (Human-As-Copilot Teleoperation System), a novel system that establishes bilateral, real-time joint synchronization between a robot arm and teleoperation hardware. This simple yet effective feedback mechanism, akin to a steering wheel in autonomous vehicles, enables the human copilot to intervene seamlessly while collecting action-correction data for future learning. Implemented using 3D-printed components and low-cost, off-the-shelf motors, HACTS is both accessible and scalable. Our experiments show that HACTS significantly enhances performance in imitation learning (IL) and reinforcement learning (RL) tasks, boosting IL recovery capabilities and data efficiency, and facilitating human-in-the-loop RL. HACTS paves the way for more effective and interactive human-robot collaboration and data-collection, advancing the capabilities of robot manipulation.
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