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Three-dimensional neural net for learning visuomotor coordination of a robot arm.

Authors
Type
Published Article
Journal
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council
Publication Date
Volume
1
Issue
1
Pages
131–136
Identifiers
PMID: 18282830
Source
Medline
License
Unknown

Abstract

An extension of T. Kohonen's (1982) self-organizing mapping algorithm together with an error-correction scheme based on the Widrow-Hoff learning rule is applied to develop a learning algorithm for the visuomotor coordination of a simulated robot arm. Learning occurs by a sequence of trial movements without the need for an external teacher. Using input signals from a pair of cameras, the closed robot arm system is able to reduce its positioning error to about 0.3% of the linear dimensions of its work space. This is achieved by choosing the connectivity of a three-dimensional lattice consisting of the units of the neural net.

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