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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
EditorsPaul F. Verschure, Alessandra Lintas, Alessandro E. Villa, Stefano Rovetta
PublisherSpringer Verlag
Pages11-18
Number of pages8
ISBN (Print)9783319685991
DOIs
Publication statusPublished - 2017 Jan 1
Externally publishedYes
Event26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
Duration: 2017 Sep 112017 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10613 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Artificial Neural Networks, ICANN 2017
CountryItaly
CityAlghero
Period17/9/1117/9/14

Keywords

  • Neurorobotics
  • Recurrent neural networks
  • Robot
  • Uncertainty

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Murata, S., Masuda, W., Tomioka, S., Ogata, T., & Sugano, S. (2017). Mixing actual and predicted sensory states based on uncertainty estimation for flexible and robust robot behavior. In P. F. Verschure, A. Lintas, A. E. Villa, & S. Rovetta (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings (pp. 11-18). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10613 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68600-4_2