Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning

Abstract: 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 real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based 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 high-dimensional 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 devil-sticking, polebalancing by a humanoid robot arm, and inverse-dynamics learning for a seven and a 30 degreeof-freedom robot. In all these examples, the application of our statis...

• Type: Journal
• Year: 2002
• Where: APIN
• Authors: Stefan Schaal, Christopher G. Atkeson, Sethu Vijayakumar
• Keywords: APIN 2002, Incremental Lwl, LWL Algorithms, Memory-based Lwl