Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control
This addresses the challenge of separating acting and reaction forces for robotics, but it is incremental as it builds on existing bilateral control techniques.
The study tackled the problem of precise object manipulation by proposing an imitation learning method that uses force and position information, verified through a line-drawing task with two neural networks.
This study proposes an imitation learning method based on force and position information. Force information is required for precise object manipulation but is difficult to obtain because the acting and reaction forces cannnot be separated. To separate the forces, we proposed to introduce bilateral control, in which the acting and reaction forces are divided using two robots. In the proposed method, two models of neural networks learn a task; to draw a line along a ruler. We verify the possibility that force information is essential to imitate the human skill of object manipulation.