Mixing actual and predicted sensory states based on uncertainty estimation for flexible and robust robot behavior

Shingo Murata*, Wataru Masuda, Saki Tomioka, Tetsuya Ogata, Shigeki Sugano

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we propose a method to dynamically modulate the input state of recurrent neural networks (RNNs) so as to realize flexible and robust robot behavior. We employ the so-called stochastic continuous-time RNN (S-CTRNN), which can learn to predict the mean and variance (or uncertainty) of subsequent sensorimotor information. Our proposed method uses this estimated uncertainty to determine a mixture ratio for combining actual and predicted sensory states of network input. The method is evaluated by conducting a robot learning experiment in which a robot is required to perform a sensory-dependent task and a sensory-independent task. The sensory-dependent task requires the robot to incorporate meaningful sensory information, and the sensory-independent task requires the robot to ignore irrelevant sensory information. Experimental results demonstrate that a robot controlled by our proposed method exhibits flexible and robust behavior, which results from dynamic modulation of the network input on the basis of the estimated uncertainty of actual sensory states.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
編集者Paul F. Verschure, Alessandra Lintas, Alessandro E. Villa, Stefano Rovetta
出版社Springer Verlag
ページ11-18
ページ数8
ISBN(印刷版)9783319685991
DOI
出版ステータスPublished - 2017
イベント26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
継続期間: 2017 9 112017 9 14

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)
10613 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other26th International Conference on Artificial Neural Networks, ICANN 2017
国/地域Italy
CityAlghero
Period17/9/1117/9/14

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)

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