Imitation Learning Based on Bilateral Control for Human-Robot Cooperation

TL;DR

This paper introduces a bilateral control-based imitation learning approach to enhance human-robot cooperation, enabling robots to adapt to dynamic interactions and subtle perturbations during collaborative tasks.

Contribution

It proposes a novel imitation learning method utilizing bilateral control to effectively manage dynamic human-robot interactions in cooperative tasks.

Findings

Force control is crucial for managing dynamic interactions.

The inferred action force enables robots to adapt to subtle perturbations.

Experimental results validate the effectiveness of the proposed method.

Abstract

Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.