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

Antonelli, Marco, Duran, Angel J., Chinellato, Eris ORCID: https://orcid.org/0000-0003-1920-2238 and Del Pobil, Angel P. (2015) Learning the visual–oculomotor transformation: effects on saccade control and space representation. Robotics and Autonomous Systems, 71 . pp. 13-22. ISSN 0921-8890 [Article] (doi:10.1016/j.robot.2014.11.018)

[img]
Preview
PDF - Final accepted version (with author's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives.

Download (789kB) | Preview

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.

Item Type: Article
Additional Information: September 2015, Emerging Spatial Competences: From Machine Perception to Sensorimotor Intelligence
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19779
Notes on copyright: © 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/
Useful Links:
Depositing User: Eris Chinellato
Date Deposited: 10 May 2016 10:36
Last Modified: 10 Jun 2021 13:44
URI: https://eprints.mdx.ac.uk/id/eprint/19779

Actions (login required)

View Item View Item

Statistics

Downloads
Activity Overview
233Downloads
349Hits

Additional statistics are available via IRStats2.