Access Restriction

Author Antonelli, M. ♦ Duran, A.J. ♦ Chinellato, E. ♦ del Pobil, A.P.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Adaptation models ♦ Visualization ♦ Brain modeling ♦ Computer architecture ♦ Robot sensing systems ♦ Computational modeling
Abstract Saccades are fast eye movements that allow humans and robots to bring the visual target in the center of the visual field. Saccades are open loop with respect to the vision system, thus their execution require a precise knowledge of the internal model of the oculomotor system. In this work, we modeled the saccade control, taking inspiration from the recurrent loops between the cerebellum and the brainstem. In this model, the brainstem acts as a fixed-inverse model of the oculomotor system, while the cerebellum acts as an adaptive element that learns the internal model of the oculomotor system. The adaptive filter is implemented using a state-of-the-art neural network, called I-SSGPR. The proposed approach, namely recurrent architecture, was validated through experiments performed both in simulation and on an antropomorphic robotic head. Moreover, we compared the recurrent architecture with another model of the cerebellum, the feedback error learning. Achieved results show that the recurrent architecture outperforms the feedback error learning in terms of accuracy and insensitivity to the choice of the feedback controller.
Description Author affiliation: Robotic Intell. Lab., Univ. Jaume I, Spain (Antonelli, M.; Duran, A.J.; del Pobil, A.P.) || Sch. of Comput., Univ. of Leeds, Leeds, UK (Chinellato, E.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-05-26
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479969234
Size (in Bytes) 354.05 kB
Page Count 6
Starting Page 5048
Ending Page 5053

Source: IEEE Xplore Digital Library