Head stabilization based on a feedback error learning in a humanoid robot

Egidio Falotico*, Nino Cauli, Kenji Hashimoto, Przemyslaw Kryczka, Atsuo Takanishi, Paolo Dario, Alain Berthoz, Cecilia Laschi

*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

In this work we propose an adaptive model for the head stabilization based on a feedback error learning (FEL). This model is capable to overcome the delays caused by the head motor system and adapts itself to the dynamics of the head motion. It has been designed to track an arbitrary reference orientation for the head in space and reject the disturbance caused by trunk motion. For efficient error learning we use the recursive least square algorithm (RLS), a Newton-like method which guarantees very fast convergence. Moreover, we implement a neural network to compute the rotational part of the head inverse kinematics. Verification of the proposed control is conducted through experiments with Matlab SIMULINK and a humanoid robot SABIAN.

本文言語English
ホスト出版物のタイトル2012 IEEE RO-MAN
ホスト出版物のサブタイトルThe 21st IEEE International Symposium on Robot and Human Interactive Communication
ページ449-454
ページ数6
DOI
出版ステータスPublished - 2012
イベント2012 21st IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2012 - Paris, France
継続期間: 2012 9月 92012 9月 13

出版物シリーズ

名前Proceedings - IEEE International Workshop on Robot and Human Interactive Communication

Conference

Conference2012 21st IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2012
国/地域France
CityParis
Period12/9/912/9/13

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能
  • 人間とコンピュータの相互作用

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