TY - JOUR T1 - Learning the visual-oculomotor transformation: Effects on saccade control and space representation JF - Robotics and Autonomous Systems Y1 - 2014 A1 - Marco Antonelli A1 - Angel J Duran A1 - Eris Chinellato A1 - Angel P. del Pobil KW - Cerebellum KW - Gaussian process regression KW - Humanoid robotics KW - Sensorimotor transformation KW - stereo vision AB -

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.

UR - http://www.sciencedirect.com/science/article/pii/S092188901400311X ER -