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Reference TypeJournal Article
Author(s)Shibata, T.;Schaal, S.
Year2001
TitleBiomimetic gaze stabilization based on feedback-error learning with nonparametric regression networks
Journal/Conference/Book TitleNeural Networks
Keywordsoculomotor control, cerebellum, VOR, OKR, learning, locally weighted learning, feedback error learning, Best Annual Paper Award of the Japanese Neural Networks Society
AbstractOculomotor control in a humanoid robot faces similar problems as biological oculomotor systems, i.e. the stabilization of gaze in face of unknown perturbations of the body, selective attention, stereo vision, and dealing with large information processing delays. Given the nonlinearities of the geometry of binocular vision as well as the possible nonlinearities of the oculomotor plant, it is desirable to accomplish accurate control of these behaviors through learning approaches. This paper develops a learning control system for the phylogenetically oldest behaviors of oculomotor control, the stabilization reflexes of gaze. In a step-wise procedure, we demonstrate how control theoretic reasonable choices of control components result in an oculomotor control system that resembles the known functional anatomy of the primate oculomotor system. The core of the learning system is derived from the biologically inspired principle of feedback-error learning combined with a state-of-the-art non-parametric statistical learning network. With this circuitry, we demonstrate that our humanoid robot is able to acquire high performance visual stabilization reflexes after about 40 s of learning despite significant nonlinearities and processing delays in the system.
Volume14
Number2
Pages201-216
Short TitleBiomimetic gaze stabilization based on feedback-error learning with nonparametric regression networks
URL(s) http://www-clmc.usc.edu/publications/S/shibata-NN2001.pdf

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