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Biomimetic gaze stabilization based on feedback-error learning with nonparametric regression networks




Oculomotor 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.

Author(s): Shibata, T. and Schaal, S.
Book Title: Neural Networks
Volume: 14
Number (issue): 2
Pages: 201-216
Year: 2001

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p1442
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/shibata-NN2001.pdf


  title = {Biomimetic gaze stabilization based on feedback-error learning with nonparametric regression networks},
  author = {Shibata, T. and Schaal, S.},
  booktitle = {Neural Networks},
  volume = {14},
  number = {2},
  pages = {201-216},
  year = {2001},
  note = {clmc},
  crossref = {p1442},
  url = {http://www-clmc.usc.edu/publications/S/shibata-NN2001.pdf}