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A System-Level Brain Model for Enactive Haptic Perception in a Humanoid Robot

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14254))

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Abstract

Perception is not a passive process but the result of an interaction between an organism and the environment. This is especially clear in haptic perception that depends entirely on tactile exploration of an object. We investigate this idea in a system-level brain model of somatosensory and motor cortex and show how it can use signals from a humanoid robot to categorize different object. The model suggests a number of critical properties that the sensorimotor system must have to support this form of enactive perception. Furthermore, we show that motor feedback during controlled movements is sufficient for haptic object categorization.

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Acknowledgments

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society (WASP-HS) funded by the Marianne and Marcus Wallenberg Foundation and the Marcus and Amalia Wallenberg Foundation.

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Authors and Affiliations

  1. Lund University Cognitive Science, Lund, Sweden

    Kristín Ósk Ingvarsdóttir, Birger Johansson, Trond A. Tjøstheim & Christian Balkenius

Authors
  1. Kristín Ósk Ingvarsdóttir
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  2. Birger Johansson
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  3. Trond A. Tjøstheim
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  4. Christian Balkenius
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Corresponding author

Correspondence to Christian Balkenius .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Ingvarsdóttir, K.Ó., Johansson, B., Tjøstheim, T.A., Balkenius, C. (2023). A System-Level Brain Model for Enactive Haptic Perception in a Humanoid Robot. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_36

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  • DOI: https://doi.org/10.1007/978-3-031-44207-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44206-3

  • Online ISBN: 978-3-031-44207-0

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