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Fast learning of biomimetic oculomotor control with nonparametric regression networks

2000

Conference Paper

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Accurate oculomotor control is one of the essential pre-requisites of successful visuomotor coordination. Given the variable nonlinearities of the geometry of binocular vision as well as the possible nonlinearities of the oculomotor plant, it is desirable to accomplish accurate oculomotor control through learning approaches. In this paper, we investigate learning control for a biomimetic active vision system mounted on a humanoid robot. By combining a biologically inspired cerebellar learning scheme with a state-of-the-art statistical learning network, our robot system is able to acquire high performance visual stabilization reflexes after about 40 seconds of learning despite significant nonlinearities and processing delays in the system.

Author(s): Shibata, T. and Schaal, S.
Book Title: International Conference on Robotics and Automation (ICRA2000)
Pages: 3847-3854
Year: 2000

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Address: San Francisco, April 2000
Cross Ref: p1281
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/shibata-ICRA2000.pdf

BibTex

@inproceedings{Shibata_ICRA_2000,
  title = {Fast learning of biomimetic oculomotor control with nonparametric regression networks},
  author = {Shibata, T. and Schaal, S.},
  booktitle = {International Conference on Robotics and Automation (ICRA2000)},
  pages = {3847-3854},
  address = {San Francisco, April 2000},
  year = {2000},
  note = {clmc},
  crossref = {p1281},
  url = {http://www-clmc.usc.edu/publications/S/shibata-ICRA2000.pdf}
}