Identification of self-body based on dynamic predictability using neuro-dynamical system

Harumitsu Nobuta*, Shun Nishide, Hiroshi G. Okuno, Tetsuya Ogata

*この研究の対応する著者

研究成果

1 被引用数 (Scopus)

抄録

The goal of our work is to acquire an internal model through a robot's experience. The internal model has the ability for mutual conversion between motor commands and movement of the body (e.g. hand) in view. Unlike other works, which assume the robot's body to be extracted in its view, we assume that external moving objects are also included in its view. We introduce predictability as a measure to segregate such objects from the robot's body: the robot's body is predictable while moving objects are not. Prediction is conducted using a neuro-dynamical system called the multiple timescales recurrent neural network (MTRNN). The prediction results of the robot's body are compared with the actual motion to distinguish the robot's body from other objects. For evaluation, we conducted an experiment with the robot moving its hand while moving objects were in view. The results of the experiment showed that the prediction of the robot's hand is 3.86 times as accurate as that of others on average. These results show the effectiveness of using predictability as a measure to acquire an internal model in an environment that includes both a robot's body and other moving objects in view.

本文言語English
ホスト出版物のタイトル2011 IEEE/SICE International Symposium on System Integration, SII 2011
ページ256-261
ページ数6
DOI
出版ステータスPublished - 2011 12 1
外部発表はい
イベント2011 IEEE/SICE International Symposium on System Integration, SII 2011 - Kyoto, Japan
継続期間: 2011 12 202011 12 22

出版物シリーズ

名前2011 IEEE/SICE International Symposium on System Integration, SII 2011

Conference

Conference2011 IEEE/SICE International Symposium on System Integration, SII 2011
国/地域Japan
CityKyoto
Period11/12/2011/12/22

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 制御およびシステム工学

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