Motion generation based on reliable predictability using self-organized object features

Shun Nishide*, Tetsuya Ogata, Jun Tani, Toru Takahashi, Kazunori Komatani, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Conference contribution

抄録

Predictability is an important factor for determining robot motions. This paper presents a model to generate robot motions based on reliable predictability evaluated through a dynamics learning model which self-organizes object features. The model is composed of a dynamics learning module, namely Recurrent Neural Network with Parametric Bias (RNNPB), and a hierarchical neural network as a feature extraction module. The model inputs raw object images and robot motions. Through bi-directional training of the two models, object features which describe the object motion are self-organized in the output of the hierarchical neural network, which is linked to the input of RNNPB. After training, the model searches for the robot motion with high reliable predictability of object motion. Experiments were performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing, and rolling motions. For objects with single motion possibility, the robot tended to generate motions that induce the object motion. For objects with two motion possibilities, the robot evenly generated motions that induce the two object motions.

本文言語English
ホスト出版物のタイトルIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
ページ3453-3458
ページ数6
DOI
出版ステータスPublished - 2010 12 1
外部発表はい
イベント23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Taipei, Taiwan, Province of China
継続期間: 2010 10 182010 10 22

出版物シリーズ

名前IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings

Conference

Conference23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010
国/地域Taiwan, Province of China
CityTaipei
Period10/10/1810/10/22

ASJC Scopus subject areas

  • 人工知能
  • 人間とコンピュータの相互作用
  • 制御およびシステム工学

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