Fast connectionist learning for trailer backing using a real robot

Author

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

Author_Institution

Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA

Volume

2

fYear

1996

fDate

22-28 Apr 1996

Firstpage

1917

Abstract

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

Keywords

mobile robots; neurocontrollers; unsupervised learning; autonomous mini-robot; connectionist control-learning system; data sharing; eligibility traces; fast connectionist learning; neural net; output responses; rapid unsupervised learning; temporal domains; topologically defined neighborhoods; trailer backing; Application software; Computer science; Control systems; Learning systems; Network topology; Neural networks; Neurofeedback; Neurons; Robots; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on

Conference_Location

Minneapolis, MN

ISSN

1050-4729

Print_ISBN

0-7803-2988-0

Type

conf

DOI

10.1109/ROBOT.1996.506991

Filename

506991