Structural feature extraction based on active sensing experiences

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

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Affordance is a feature of an object or environment that implies how to interact with it. Based on affordance theory, humans are said to perceive invariant structures for cognizing the object/environment for generating behaviors. In this paper, the authors present a method to extract invariant structures of objects from visual raw images, based on object manipulation experiences using a humanoid robot. The method consists of two training phases. The first phase utilizes Recurrent Neural Network with Parametric Bias (RNNPB) to self-organize dynamical object features extracted during active sensing with objects. The second phase trains a hierarchical neural network attached to RNNPB for associating object images and robot motions with self-organized object features. Analysis of the model has uncovered static objects features that are closely related to dynamic object motions, such as round or stable.

本文言語English
ホスト出版物のタイトルProceedings - International Conference on Informatics Education and Research for Knowledge-Circulating Society, ICKS 2008
ページ169-172
ページ数4
DOI
出版ステータスPublished - 2008 8月 29
外部発表はい
イベントInternational Conference on Informatics Education and Research for Knowledge-Circulating Society, ICKS 2008 - Kyoto, Japan
継続期間: 2008 1月 172008 1月 17

出版物シリーズ

名前Proceedings - International Conference on Informatics Education and Research for Knowledge-Circulating Society, ICKS 2008

Other

OtherInternational Conference on Informatics Education and Research for Knowledge-Circulating Society, ICKS 2008
国/地域Japan
CityKyoto
Period08/1/1708/1/17

ASJC Scopus subject areas

  • 情報システム
  • 教育

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