HACTS: 人間を副操縦士とするロボット学習用遠隔操作システム
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning
ロボットアームと遠隔操作デバイスの双方向リアルタイム関節同期を実現し、人間が介入しながら修正データを収集できる低コストなシステムを開発した。模倣学習と強化学習の性能を向上させる。
著者: Zhiyuan Xu, Yinuo Zhao, Kun Wu, Ning Liu, Junjie Ji, Zhengping Che, Chi Harold Liu, Jian Tang
分類: cs.RO, cs.LG
原文アブストラクト
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.
