Rapid unsupervised connectionist learning for backing a robot with two trailers

Dean F. Hougen, Maria L Gini, James Slagle

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

This paper presents an application of a connectionist control-learning system designed for use on an autonomous mini-robot. This system was formerly shown to form useful two-dimensional mappings rapidly when applied to backing a car with a single trailer. In the current paper the learning system is extended to three dimensions and applied to a similar but significantly more difficult problem. The system is shown to be capable of rapid unsupervised learning of output responses in temporal domains through the use of eligibility traces and inter-neural cooperation within topologically defined neighborhoods.

Original languageEnglish (US)
Pages (from-to)2950-2955
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume4
StatePublished - Jan 1 1997

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