Literature DB >> 29940487

Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience.

Alban Laflaquière1, J Kevin O'Regan2, Bruno Gas3, Alexander Terekhov4.   

Abstract

In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face. We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment. By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving. We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object. Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  Developmental robotics; Grounding; Sensorimotor contingencies; Space perception

Mesh:

Year:  2018        PMID: 29940487     DOI: 10.1016/j.neunet.2018.06.001

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  3 in total

1.  Space Emerges from What We Know-Spatial Categorisations Induced by Information Constraints.

Authors:  Nicola Catenacci Volpi; Daniel Polani
Journal:  Entropy (Basel)       Date:  2020-10-19       Impact factor: 2.524

2.  A Formal Account of Structuring Motor Actions With Sensory Prediction for a Naive Agent.

Authors:  Jean-Merwan Godon; Sylvain Argentieri; Bruno Gas
Journal:  Front Robot AI       Date:  2020-12-01

3.  The Language of Vision.

Authors:  Patrick Cavanagh
Journal:  Perception       Date:  2021-02-14       Impact factor: 1.490

  3 in total

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