Effective motion learning for a flexible-joint robot using motor babbling

Kuniyuki Takahashi, Tetsuya Ogata, Hiroki Yamada, Hadi Tjandra, Shigeki Sugano

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

We propose a method for realizing effective dynamic motion learning in a flexible-joint robot using motor babbling. Flexible-joint robots have recently attracted attention because of their adaptiveness, safety, and, in particular, dynamic motions. It is difficult to control robots that require dynamic motion. In past studies, attractors and oscillators were designed as motion primitives of an assumed task in advance. However, it is difficult to adapt to unintended environmental changes using such methods. To overcome this problem, we use a recurrent neural network (RNN) that does not require predetermined parameters. In this research, we propose a method for facilitating effective learning. First, a robot learns simple motions via motor babbling, acquiring body dynamics using a recurrent neural network (RNN). Motor babbling is the process of movement that infants use to acquire their own body dynamics during their early days. Next, the robot learns additional motions required for a target task using the acquired body dynamics. For acquiring these body dynamics, the robot uses motor babbling with its redundant flexible joints to learn motion primitives. This redundancy implies that there are numerous possible motion patterns. In comparison to a basic learning task, the motion primitives are simply modified to adjust to the task. Next, we focus on the types of motions used in motor babbling. We classify the motions into two motion types, passive motion and active motion. Passive motion involves inertia without any torque input, whereas active motion involves a torque input. The robot acquires body dynamics from the passive motion and a means of torque generation from the active motion. As a result, we demonstrate the importance of performing prior learning via motor babbling before learning a task. In addition, task learning is made more efficient by dividing the motion into two types of motor babbling patterns.

本文言語English
ホスト出版物のタイトルIROS Hamburg 2015 - Conference Digest
ホスト出版物のサブタイトルIEEE/RSJ International Conference on Intelligent Robots and Systems
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2723-2728
ページ数6
ISBN(電子版)9781479999941
DOI
出版ステータスPublished - 2015 12 11
イベントIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
継続期間: 2015 9 282015 10 2

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems
2015-December
ISSN(印刷版)2153-0858
ISSN(電子版)2153-0866

Other

OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
国/地域Germany
CityHamburg
Period15/9/2815/10/2

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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