Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth

Abstract

Thegeneration of robot motions in the real world is difficult by using conventional controllers alone and requires highly intelligent processing. In this regard, learning-based motion generations are currently being investigated. However, the main issue has been improvements of the adaptability to spatially varying environments, but a variation of the operating speed has not been investigated in detail. In contact-rich tasks, it is especially important to be able to adjust the operating speed because a nonlinear relationship occurs between the operating speed and force (e.g., inertial and frictional forces), and it affects the results of the tasks. Therefore, in this study, we propose a method for generating variable operating speeds while adapting to spatial perturbations in the environment. The proposed method can be adapted to nonlinearities by utilizing a small amount of motion data. We experimentally evaluated the proposed method by erasing a line using an eraser fixed to the tip of the robot as an example of a contact-rich task. Furthermore, the proposed method enables a robot to perform a task faster than a human operator and is capable of operating close to the control bandwidth.

Keywords

• Imitation learning
• bilateral control
• motion planning
• fast-forward
• machine learning