Collaborative Assembly Policy Learning of a Sightless Robot

TL;DR

This paper introduces a novel reinforcement learning approach combined with admittance control for sightless robot-human collaboration, improving success rates and reducing human effort in a physical board-insertion task.

Contribution

It proposes a new RL method that leverages a human-designed admittance controller to enhance robot assistance in collaborative tasks with safety constraints.

Findings

Outperforms traditional admittance control in success rate and completion time

Reduces measured force/torque during task execution

Demonstrates effectiveness in both simulation and real-world experiments

Abstract

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.