Applying intrinsic motivation for visuomotor learning of robot arm motion

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

研究成果: Conference contribution

抄録

In this paper, we present a method to apply intrinsic motivation for improving visuomotor learning of robot's arm with external object in view. Multiple Timescales Recurrent Neural Network (MTRNN) is utilized for learning the robot arm/external object dynamics. Training of MTRNN is done using the Back Propagation Through Time (BPTT) algorithm. BPTT algorithm is modified as follows. 1. Evaluate predictability of robot arm/objects using training error of MTRNN. 2. Assign a preference ratio to each object based on predictability. The preference ratio represents the weight of each object to training. Experiments were conducted using an actual robot moving the arm while a human moves his arm in the robot's camera view. The result of the experiment showed that the proposed method presents better training result of robot arm visuomotor dynamics compared to general training with BPTT.

本文言語English
ホスト出版物のタイトル2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ364-367
ページ数4
ISBN(電子版)9781479953325
DOI
出版ステータスPublished - 2014
イベント2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014 - Kuala Lumpur, Malaysia
継続期間: 2014 11 122014 11 15

出版物シリーズ

名前2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014

Other

Other2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2014
国/地域Malaysia
CityKuala Lumpur
Period14/11/1214/11/15

ASJC Scopus subject areas

  • 人工知能
  • 人間とコンピュータの相互作用

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