Active sensing based dynamical object feature extraction

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

*この研究の対応する著者

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper presents a method to autonomously extract object features that describe their dynamics from active sensing experiences. 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 extracting object features that describe the object motions. The two modules are simultaneously trained 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 extracting features that distinguish the characteristics of object dynamics.

本文言語English
ホスト出版物のタイトル2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
ページ1-7
ページ数7
DOI
出版ステータスPublished - 2008 12 1
外部発表はい
イベント2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Nice, France
継続期間: 2008 9 222008 9 26

出版物シリーズ

名前2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Conference

Conference2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
国/地域France
CityNice
Period08/9/2208/9/26

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
  • 制御およびシステム工学
  • 電子工学および電気工学

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引用スタイル