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Sensorimotor Prediction with Neural Networks on Continuous Spaces

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Abstract

In the context of Developmental Robotics, we propose to learn how the sensations of a robot are modified by its action. Many theories of Artificial Intelligence argue that sensorimotor prediction is a fundamental building block of cognition. In this paper, we learn the sensorimotor prediction on data captured by a mobile robot equipped with distance sensors. We show that Neural Networks can learn the sensorimotor regularities and perform sensorimotor prediction on continuous sensor and motor spaces.

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Author information

Authors and Affiliations

  1. SoftBank Robotics Europe - AI Lab, 43, rue du Colonel Pierre Avia, 75015, Paris, France

    Michaël Garcia Ortiz

Authors
  1. Michaël Garcia Ortiz
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Correspondence to Michaël Garcia Ortiz .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Ortiz, M.G. (2017). Sensorimotor Prediction with Neural Networks on Continuous Spaces. 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_10

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  • DOI: https://doi.org/10.1007/978-3-319-68600-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68599-1

  • Online ISBN: 978-3-319-68600-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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