Fast connectionist learning for trailer backing using a real robot

Dean F. Hougen, John Fischer, Maria L Gini, James Slagle

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


This paper presents the application of a connectionist control-learning system to an autonomous mini-robot. The system's design is severely constrained by the computing power and memory available on board the mini-robot and the on-board training time is greatly limited by the short life of the battery. The system is capable of rapid unsupervised learning of output responses in temporal domains through the use of eligibility traces and data sharing within topologically defined neighborhoods.

Original languageEnglish (US)
Pages (from-to)1917-1922
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Jan 1 1996


Dive into the research topics of 'Fast connectionist learning for trailer backing using a real robot'. Together they form a unique fingerprint.

Cite this