Imitation Learning Based on Bilateral Control for Human-Robot Cooperation
This work addresses the problem of enabling robots to handle subtle perturbations and dynamic interactions in cooperative tasks with humans, representing an incremental advancement in imitation learning for robotics.
The paper tackles the challenge of human-robot cooperation by using imitation learning based on bilateral control to extract human skills for controlling dynamic interactions, achieving successful results in a food-serving task where inferred action forces effectively manage these interactions.
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.