人机协同交互学习让机器人更高效掌握双手操作技能
RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation
- 通过双向柔顺遥操作实现人与机器策略无缝切换
- 在仿真与真实硬件上验证了复杂双臂任务学习效率提升
- 适合需要精细人机协作的机器人操作场景
从人类示范中学习是掌握复杂操作技能的有效方法。然而，现有方法主要依赖被动示范数据，因其采集简单。交互式人类教学具有理论和实践优势，但受限于现有人机接口支持不足。本文提出一种新系统，实现双臂操作任务中人类与自主策略的无缝控制切换，提升新任务学习效率。该系统基于柔顺、双向遥操作设计。通过仿真与硬件实验，验证了其在交互式人机教学中学习复杂双臂操作技能的有效性。
原文摘要 · Abstract (English)
Learning from human demonstration is an effective approach for learning complex manipulation skills. However, existing approaches heavily focus on learning from passive human demonstration data for its simplicity in data collection. Interactive human teaching has appealing theoretical and practical properties, but they are not well supported by existing human-robot interfaces. This paper proposes a novel system that enables seamless control switching between human and an autonomous policy for bi-manual manipulation tasks, enabling more efficient learning of new tasks. This is achieved through a compliant, bilateral teleoperation system. Through simulation and hardware experiments, we demonstrate the value of our system in an interactive human teaching for learning complex bi-manual manipulation skills.
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