3D-neural-net for learning visuomotor-coordination of a robot arm

Thomas M. Martinetz, Helge J. Ritter, Klaus J. Schulten

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An extension of T. Kohonen's (Biol. Cybern., vol. 43, pp. 59-69, 1982; vol. 44, pp. 135-140, 1982) self-organizing mapping algorithm together with an error-correction rule of the Widrow-Hoff type is applied to develop an unsupervised learning scheme for the visuo-motor coordination of a simulated robot arm. 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 3-D lattice between the units of the neural net.

Original languageEnglish (US)
Title of host publicationIJCNN Int Jt Conf Neural Network
Editors Anon
PublisherPubl by IEEE
Pages351-356
Number of pages6
StatePublished - 1989
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period6/18/896/22/89

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Martinetz, T. M., Ritter, H. J., & Schulten, K. J. (1989). 3D-neural-net for learning visuomotor-coordination of a robot arm. In Anon (Ed.), IJCNN Int Jt Conf Neural Network (pp. 351-356). Publ by IEEE.