Scalable techniques from nonparameteric statistics for real-time robot learning
Stefan Schaal, Chris Atkeson and Stefan Schaal
Abstract of paper published in Applied Intelligence.
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, pole-balancing by a humanoid robot
arm, and inverse-dynamics learning for a seven and a 30 degree-of-freedom 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.
Click here to download an gzip-ed version of the paper. Click here for a pdf version.
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