On-line Learning for Humanoid Robot Systems

Humanoid robots are high-dimensional 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 model-based nonlinear controllers which use functions in the control loop that are estimated by learning algorithms. However, internal models for humanoid systems are rather high-dimensional 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 real-time. 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 d...

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Gaurav Tevatia, Jörg Conradt, Sethu Vijayakumar, Stefan Schaal
• Keywords: Conventional Learning Algorithms, ICML 2000, Machine Learning, Statistical Learning Algorithm, Supervised Learning Methods