Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
 Stefan Schaal,
 Christopher G. Atkeson,
 Sethu Vijayakumar
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Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in realtime learning of complex robot tasks. We discuss two major classes of LWL, memorybased LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional belief that LWL methods cannot work well in highdimensional spaces, we provide new algorithms that have been tested on up to 90 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devilsticking, polebalancing by a humanoid robot arm, and inversedynamics learning for a seven and a 30 degreeoffreedom robot. In all these examples, the application of our statistical neural networks techniques allowed either faster or more accurate acquisition of motor control than classical control engineering.
 Title
 Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
 Journal

Applied Intelligence
Volume 17, Issue 1 , pp 4960
 Cover Date
 200207
 DOI
 10.1023/A:1015727715131
 Print ISSN
 0924669X
 Online ISSN
 15737497
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 nonparametric regression
 locally weighted learning
 motor control
 internal models
 incremental learning
 Industry Sectors
 Authors

 Stefan Schaal ^{(1)} ^{(2)}
 Christopher G. Atkeson ^{(3)} ^{(4)}
 Sethu Vijayakumar ^{(1)} ^{(2)}
 Author Affiliations

 1. Computer Science and Neuroscience, HNB103, University of Southern California, Los Angeles, CA, 900892520, USA;
 2. Kawato Dynamic Brain Project (ERATO/JST), 22 Hikaridai, Seikacho, Sorakugun, 61902, Kyoto, Japan
 3. College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA, 303320280, USA;
 4. ATR Human Information Processing Laboratories, 22 Hikaridai, Seikacho, Sorakugun, 61902, Kyoto, Japan