HACTS：一种用于机器人学习的人作为副驾驶遥操作系统 (HACTS: a Human-As-Copilot Teleoperation System for Robot Learning)

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.

翻译：遥操作对于自主机器人学习至关重要，尤其是在需要人类演示或修正的操作任务中。然而，现有系统大多仅提供单向机器人控制，缺乏将机器人状态与遥操作硬件同步的能力，从而无法实现实时、灵活的干预。本研究提出了HACTS（人作为副驾驶遥操作系统），这是一种新颖的系统，可在机械臂与遥操作硬件之间建立双向、实时的关节同步。这种简单而有效的反馈机制，类似于自动驾驶汽车中的方向盘，使人类副驾驶能够无缝介入，同时收集用于未来学习的动作修正数据。HACTS采用3D打印组件和低成本商用电机实现，兼具易获取性和可扩展性。实验表明，HACTS显著提升了模仿学习（IL）和强化学习（RL）任务的性能，增强了IL的恢复能力和数据效率，并促进了人在环路的RL。HACTS为更有效、更具交互性的人机协作与数据收集开辟了道路，推动了机器人操作能力的发展。