Learning the visual–oculomotor transformation: effects on saccade control and space representation

Article

Antonelli, M., Duran, A., Chinellato, E. and Del Pobil, A. 2015. Learning the visual–oculomotor transformation: effects on saccade control and space representation. Robotics and Autonomous Systems. 71, pp. 13-22. https://doi.org/10.1016/j.robot.2014.11.018
TypeArticle
TitleLearning the visual–oculomotor transformation: effects on saccade control and space representation
AuthorsAntonelli, M., Duran, A., Chinellato, E. and Del Pobil, A.
Abstract

Active eye movements can be exploited to build a visuomotor representation of the surrounding environment. Maintaining and improving such representation requires to update the internal model involved in the generation of eye movements. From this perspective, action and perception are thus tightly coupled and interdependent. In this work, we encoded the internal model for oculomotor control with an adaptive filter inspired by the functionality of the cerebellum. Recurrent loops between a feed-back controller and the internal model allow our system to perform accurate binocular saccades and create an implicit representation of the nearby space. Simulation results show that this recurrent architecture outperforms classical feedback-error-learning in terms of both accuracy and sensitivity to system parameters. The proposed approach was validated implementing the framework on an anthropomorphic robotic head.

LanguageEnglish
PublisherElsevier
JournalRobotics and Autonomous Systems
ISSN0921-8890
Publication dates
Online29 Dec 2014
Print01 Sep 2015
Publication process dates
Deposited10 May 2016
Accepted26 Nov 2014
Output statusPublished
Accepted author manuscript
License
Copyright Statement

© 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Additional information

September 2015, Emerging Spatial Competences: From Machine Perception to Sensorimotor Intelligence

Digital Object Identifier (DOI)https://doi.org/10.1016/j.robot.2014.11.018
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