Self-organization of dynamic object features based on bidirectional training

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

*この研究の対応する著者

研究成果: Article査読

3 被引用数 (Scopus)

抄録

This paper presents a method to self-organize object features that describe object dynamics using bidirectional training. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for self-organizing object features that describe the object motions. The two modules are simultaneously trained through bidirectional training using image and motion sequences acquired from the robot's active sensing with objects. Experiments are performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing and rolling motions. The results have shown that the model is capable of self-organizing object dynamics based on the self-organized features.

本文言語English
ページ(範囲)2035-2057
ページ数23
ジャーナルAdvanced Robotics
23
15
DOI
出版ステータスPublished - 2009 10月 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用

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