Tool-body assimilation model using a neuro-dynamical system for acquiring representation of tool function and motion

Kuniyuki Takahshi, Tetsuya Ogata, Hadi Tjandra, Yuki Yamaguchi*, Yuki Suga, Shigeki Sugano

*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

In this paper, we propose a tool-body assimilation model that implements a multiple time-scales recurrent neural network (MTRNN). Our model allows a robot to acquire the representation of a tool function and the required motion without having any prior knowledge of the tool. It is composed of five modules: image feature extraction, body model, tool dynamics feature, tool recognition, and motion recognition. Self-organizing maps (SOM) are used for image feature extraction from raw images. The MTRNN is used for body model learning. Parametric bias (PB) nodes are used to learn tool dynamic features. The PB nodes are attached to the neurons of the MTRNN to modulate the body model. A hierarchical neural network (HNN) is implemented for tool and motion recognition. Experiments were conducted using OpenHRP3, a robotics simulator, with multiple tools. The results show that the tool-body assimilation model is capable of recognizing tools, including those having an unlearned shape, and acquires the required motions accordingly.

本文言語English
ホスト出版物のタイトルAIM 2014 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1255-1260
ページ数6
ISBN(印刷版)9781479957361
DOI
出版ステータスPublished - 2014
イベント2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2014 - Besancon, France
継続期間: 2014 7 82014 7 11

出版物シリーズ

名前IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Conference

Conference2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2014
国/地域France
CityBesancon
Period14/7/814/7/11

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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