Imitation Learning Based on Bilateral Control for Human-Robot Cooperation

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.

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References in corpus (1)

• Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

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• A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Control
• Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
• Imitation learning for variable speed motion generation over multiple actions
• Improving Low-Cost Teleoperation: Augmenting GELLO with Force