Learning the visual-oculomotor transformation: Effects on saccade control and space representation

TitleLearning the visual-oculomotor transformation: Effects on saccade control and space representation
Publication TypeJournal Article
Year of Publication2014
AuthorsAntonelli, M, Duran, AJ, Chinellato, E, del Pobil, AP
JournalRobotics and Autonomous Systems
ISSN09218890
KeywordsCerebellum, Gaussian process regression, Humanoid robotics, Sensorimotor transformation, stereo vision
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. Simulations 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.

URLhttp://www.sciencedirect.com/science/article/pii/S092188901400311X
DOI10.1016/j.robot.2014.11.018