Efficient motor babbling using variance predictions from a recurrent neural network

Kuniyuki Takahashi*, Kanata Suzuki, Tetsuya Ogata, Hadi Tjandra, Shigeki Sugano

*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

We propose an exploratory form of motor babbling that uses variance predictions from a recurrent neural network as a method to acquire the body dynamics of a robot with flexible joints. In conventional research methods, it is difficult to construct real robots because of the large number of motor babbling motions required. In motor babbling, different motions may be easy or difficult to predict. The variance is large in difficult-to-predict motions, whereas the variance is small in easy-topredict motions. We use a Stochastic Continuous Timescale Recurrent Neural Network to predict the accuracy and variance of motions. Using the proposed method, a robot can explore motions based on variance. To evaluate the proposed method, experiments were conducted in which the robot learns crank turning and door opening/closing tasks after exploring its body dynamics. The results show that the proposed method is capable of efficient motion generation for any given motion tasks.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
編集者Tingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
出版社Springer Verlag
ページ26-33
ページ数8
ISBN(印刷版)9783319265544
DOI
出版ステータスPublished - 2015
イベント22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
継続期間: 2015 11月 92015 11月 12

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9491
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other22nd International Conference on Neural Information Processing, ICONIP 2015
国/地域Turkey
CityIstanbul
Period15/11/915/11/12

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

フィンガープリント

「Efficient motor babbling using variance predictions from a recurrent neural network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル