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Autonomous Robots
Erschienen in: Autonomous Robots 1/2013

01.07.2013

A biologically constrained architecture for developmental learning of eye–head gaze control on a humanoid robot

verfasst von: James Law, Patricia Shaw, Mark Lee

Erschienen in: Autonomous Robots | Ausgabe 1/2013

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Abstract

In this paper we describe a biologically constrained architecture for developmental learning of eye–head gaze control on an iCub robot. In contrast to other computational implementations, the developmental approach aims to acquire sensorimotor competence through growth processes modelled on data and theory from infant psychology. Constraints help shape learning in infancy by limiting the complexity of interactions between the body and environment, and we use this idea to produce efficient, effective learning in autonomous robots. Our architecture is based on current thinking surrounding the gaze mechanism, and experimentally derived models of stereotypical eye–head gaze contributions. It is built using our proven constraint-based field-mapping approach. We identify stages in the development of infant gaze control, and propose a framework of artificial constraints to shape learning on the robot in a similar manner. We demonstrate the impact these constraints have on learning, and the resulting ability of the robot to make controlled gaze shifts.
Vorheriger Artikel Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

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Metadaten
Titel
A biologically constrained architecture for developmental learning of eye–head gaze control on a humanoid robot
verfasst von
James Law
Patricia Shaw
Mark Lee
Publikationsdatum
01.07.2013
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 1/2013
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-013-9335-2

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