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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2023
PublisherSpringer
Pages432-443
Number of pages11
ISBN (Electronic)978-3-031-44207-0
ISBN (Print)978-3-031-44206-3
DOIs
Publication statusPublished - 2023 Sept 22
EventThe 32nd International Conference on Artificial Neural Networks (ICANN 2023) - Heraklion, Grekland
Duration: 2023 Sept 262023 Sept 29
https://e-nns.org/icann2023/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume14254
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe 32nd International Conference on Artificial Neural Networks (ICANN 2023)
Abbreviated titleICANN 2023
Period2023/09/262023/09/29
Internet address

Subject classification (UKÄ)

  • Robotics

Free keywords

  • humanoid robot
  • Object categorization
  • haptic perception
  • Affordances
  • Enactive perception

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