Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot

TL;DR

This paper introduces a novel method for applying bilateral control-based imitation learning to velocity-controlled robots, enabling robust object manipulation through force control, verified by experimental mopping tasks.

Contribution

The paper proposes a new approach to implement bilateral control-based imitation learning on velocity-controlled robots, expanding its applicability beyond torque-controlled systems.

Findings

Effective imitation learning for velocity-controlled robots.

Successful experimental validation with a mopping task.

Enhanced robustness in object manipulation.

Abstract

Machine learning is now playing important role in robotic object manipulation. In addition, force control is necessary for manipulating various objects to achieve robustness against perturbations of configurations and stiffness. The author's group revealed that fast and dynamic object manipulation with force control can be obtained by bilateral control-based imitation learning. However, the method is applicable only in robots that can control torque, while it is not applicable in robots that can only follow position or velocity commands like many commercially available robots. Then, in this research, a way to implement bilateral control-based imitation learning to velocity-controlled robots is proposed. The validity of the proposed method is experimentally verified by a mopping task.