

Publication Details
Reference Type  Conference Proceedings 
Author(s)  Conradt, J.;Tevatia, G.;Vijayakumar, S.;Schaal, S. 
Year  2000 
Title  Online learning for humanoid robot systems 
Journal/Conference/Book Title  Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000) 
Keywords  humanoid robotics, online learning, nonparametric regression, supersmoothing, incremental 
Abstract  Humanoid robots are highdimensional movement systems for which analytical system identification and control methods are insufficient due to unknown nonlinearities in the system structure. As a way out, supervised learning methods can be employed to create modelbased nonlinear controllers which use functions in the control loop that are estimated by learning algorithms. However, internal models for humanoid systems are rather highdimensional such that conventional learning algorithms would suffer from slow learning speed, catastrophic interference, and the curse of dimensionality. In this paper we explore a new statistical learning algorithm, locally weighted projection regression (LWPR), for learning internal models in realtime. LWPR is a nonparametric spatially localized learning system that employs the less familiar technique of partial least squares regression to represent functional relationships in a piecewise linear fashion. The algorithm can work successfully in very high dimensional spaces and detect irrelevant and redundant inputs while only requiring a computational complexity that is linear in the number of input dimensions. We demonstrate the application of the algorithm in learning two classical internal models of robot control, the inverse kinematics and the inverse dynamics of an actual seven degreeoffreedom anthropomorphic robot arm. For both examples, LWPR can achieve excellent realtime learning results from less than one hour of actual training data. 
Place Published  Stanford, CA 
Volume  1 
Pages  191198 
Short Title  Online learning for humanoid robot systems 
URL(s)  http://wwwclmc.usc.edu/publications/C/conradtICML2000.pdf


