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.
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Acknowledgement
This work was supported in part by JST CREST Grant Number: JPMJCR15E3, Japan;JSPS KAKENHI Grant Numbers: 25220005, 17K12754, Japan and the “Fundamental Study for Intelligent Machine to Coexist with Nature” program of the Research Institute for Science and Engineering at Waseda University, Japan.
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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: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_2
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DOI: https://doi.org/10.1007/978-3-319-68600-4_2
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