DocumentCode :
3011026
Title :
Local Online Support Vector Regression for Learning Control
Author :
Choi, Younggeun ; Cheong, Shin-Young ; Schweighofer, Nicolas
Author_Institution :
Dept. of Comput. Sci., Los Angeles
fYear :
2007
fDate :
20-23 June 2007
Firstpage :
13
Lastpage :
18
Abstract :
Support vector regression (SVR) is a class of machine learning technique that has been successfully applied to low-level learning control in robotics. Because of the large amount of computation required by SVR, however, most studies have used a batch mode. Although a recently developed online form of SVR shows faster learning performance than batch SVR, the amount of computation required by online SVR prevent its use in real-time robot learning control, which requires short sampling time. Here, we present a novel method, Local online SVR for Learning control, or LoSVR, that extends online SVR with a windowing method. We demonstrate the performance of LoSVR in learning the inverse dynamics of both a simulated two-joint robot and a real one-link robot arm. Our results show that, in both cases, LoSVR can learn the inverse dynamics on-line faster and with a better accuracy than batch SVR.
Keywords :
learning (artificial intelligence); learning systems; manipulator dynamics; regression analysis; support vector machines; local online support vector regression; machine learning technique; one-link robot arm; real-time robot learning control; simulated two-joint robot inverse dynamics; Computational intelligence; Degradation; Machine learning; Neural networks; Robot control; Robotics and automation; Sampling methods; Support vector machine classification; Support vector machines; USA Councils;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Robotics and Automation, 2007. CIRA 2007. International Symposium on
Conference_Location :
Jacksonville, FI
Print_ISBN :
1-4244-0790-7
Electronic_ISBN :
1-4244-0790-7
Type :
conf
DOI :
10.1109/CIRA.2007.382883
Filename :
4269883
Link To Document :
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