DocumentCode :
775769
Title :
Machine Learning With AIBO Robots in the Four-Legged League of RoboCup
Author :
Chalup, Stephan K. ; Murch, Craig L. ; Quinlan, Michael J.
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW
Volume :
37
Issue :
3
fYear :
2007
fDate :
5/1/2007 12:00:00 AM
Firstpage :
297
Lastpage :
310
Abstract :
Robot learning is a growing area of research at the intersection of robotics and machine learning. The main contributions of this paper include a review of how machine learning has been used on Sony AIBO robots and at RoboCup, with a focus on the four-legged league during the years 1998-2004. The review shows that the application-oriented use of machine learning in the four-legged league was still conservative and restricted to a few well-known and easy-to-use methods such as standard decision trees, evolutionary hill climbing, and support vector machines. Method-oriented spin-off studies emerged more frequently and increasingly addressed new and advanced machine learning techniques. Further, the paper presents some details about the growing impact of machine learning in the software system developed by the authors´ robot soccer team-the NUbots
Keywords :
decision trees; evolutionary computation; learning (artificial intelligence); legged locomotion; multi-robot systems; sport; support vector machines; NUbots software system; RoboCup; Sony AIBO robots; decision trees; evolutionary hill climbing; four-legged league; machine learning; robot learning; support vector machines; Australia; Computational modeling; Computer science; Hardware; Humans; Intelligent robots; Legged locomotion; Machine learning; Robot vision systems; Software systems; Learning systems; RoboCup; Sony AIBO; legged locomotion; machine learning; robot soccer; robot vision systems;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
Publisher :
ieee
ISSN :
1094-6977
Type :
jour
DOI :
10.1109/TSMCC.2006.886964
Filename :
4154938
Link To Document :
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