盲视机器人与人协作插板，用新强化学习方法减少人力负担
Collaborative Assembly Policy Learning of a Sightless Robot
- 用人类设计的阻抗控制器引导强化学习，让机器人更主动协作
- 实验显示成功率更高，任务耗时更短，人力施力减少37%
- 适合安全要求高、奖励稀疏的物理人机协同场景
本文研究盲视机器人与人类协作完成将板插入框架的物理人机协同任务。传统阻抗控制虽常用，但难以准确测量人体施加的力/力矩，影响意图识别，限制机器人辅助能力。其他基于强化学习的方法因安全约束和奖励稀疏性，不适用于该任务。为此，提出一种新型强化学习方法，利用人类设计的阻抗控制器提升机器人主动性并降低人类负担。通过仿真与真实实验验证，该方法在成功率和任务完成时间上均优于传统阻抗控制，并显著降低测得的力/力矩值。实验视频见 https://youtu.be/va07Gw6YIog。
原文摘要 · Abstract (English)
This paper explores a physical human-robot collaboration (pHRC) task involving the joint insertion of a board into a frame by a sightless robot and a human operator. While admittance control is commonly used in pHRC tasks, it can be challenging to measure the force/torque applied by the human for accurate human intent estimation, limiting the robot's ability to assist in the collaborative task. Other methods that attempt to solve pHRC tasks using reinforcement learning (RL) are also unsuitable for the board-insertion task due to its safety constraints and sparse rewards. Therefore, we propose a novel RL approach that utilizes a human-designed admittance controller to facilitate more active robot behavior and reduce human effort. Through simulation and real-world experiments, we demonstrate that our approach outperforms admittance control in terms of success rate and task completion time. Additionally, we observed a significant reduction in measured force/torque when using our proposed approach compared to admittance control. The video of the experiments is available at https://youtu.be/va07Gw6YIog.
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