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Proceedings Paper

Neural Controller For Adaptive Sensory-Motor Coordination
Author(s): Michael Kuperstein; Jorge Rubinstein
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Paper Abstract

We present a theory and prototype of a neural controller called INFANT that learns sensory-motor coordination from its own experience. INFANT adapts to unforeseen changes in the geometry of the physical motor system and to the location, orientation, shape and size of objects. It can learn to accurately grasp an elongated object without any information about the geometry of the physical sensory-motor system. This new neural controller relies on the self-consistency between sensory and motor signals to achieve unsupervised learning. It is designed to be generalized for coordinating any number of sensory inputs with limbs of any number of joints. INFANT is implemented with an image processor, stereo cameras and a five degree-of freedom robot arm. Its average grasping accuracy after learning is 3% of the arm's length in position and 6 degrees in orientation.

Paper Details

Date Published: 27 March 1989
PDF: 13 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960328
Show Author Affiliations
Michael Kuperstein, Neurogen (United States)
Jorge Rubinstein, Neurogen (United States)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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