Modeling tool-body assimilation using second-order recurrent neural network

Shun Nishide*, Tatsuhiro Nakagawa, Tetsuya Ogata, Jun Tani, Toru Takahashi, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

Tool-body assimilation is one of the intelligent human abilities. Through trial and experience, humans are capable of using tools as if they are part of their own bodies. This paper presents a method to apply a robot's active sensing experience for creating the tool-body assimilation model. The model is composed of a feature extraction module, dynamics learning module, and a tool recognition module. Self-Organizing Map (SOM) is used for the feature extraction module to extract object features from raw images. Multiple Time-scales Recurrent Neural Network (MTRNN) is used as the dynamics learning module. Parametric Bias (PB) nodes are attached to the weights of MTRNN as second-order network to modulate the behavior of MTRNN based on the tool. The generalization capability of neural networks provide the model the ability to deal with unknown tools. Experiments are performed with HRP-2 using no tool, I-shaped, T-shaped, and L-shaped tools. The distribution of PB values have shown that the model has learned that the robot's dynamic properties change when holding a tool. The results of the experiment show that the tool-body assimilation model is capable of applying to unknown objects to generate goal-oriented motions.

本文言語English
ホスト出版物のタイトル2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
ページ5376-5381
ページ数6
DOI
出版ステータスPublished - 2009 12 11
外部発表はい
イベント2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO, United States
継続期間: 2009 10 112009 10 15

出版物シリーズ

名前2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

Conference

Conference2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
国/地域United States
CitySt. Louis, MO
Period09/10/1109/10/15

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 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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