Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control

Tsuyoshi Adachi; Kazuki Fujimoto; Sho Sakaino; Toshiaki Tsuji

IROS 2018poster57 citations

Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control

Tsuyoshi Adachi, Kazuki Fujimoto, Sho Sakaino, Toshiaki Tsuji

Abstract

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 cannot 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.

BibTeX

@inproceedings{iros2018_imitationlearnin,
  title = {Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control},
  author = {Tsuyoshi Adachi and Kazuki Fujimoto and Sho Sakaino and Toshiaki Tsuji},
  booktitle = {IROS 2018},
  year = {2018}
}