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2007

Learning omnidirectional path following using dimensionality reduction

10 years 1 months ago
Learning omnidirectional path following using dimensionality reduction
Abstract— We consider the task of omnidirectional path following for a quadruped robot: moving a four-legged robot along any arbitrary path while turning in any arbitrary manner. Learning a controller capable of such motion requires learning the parameters of a very high-dimensional policy class, which requires a prohibitively large amount of data to be collected on the real robot. Although learning such a policy can be much easier in a model (or “simulator”) of the system, it can be extremely difficult to build a sufficiently accurate simulator. In this paper we propose a method that uses a (possibly inaccurate) simulator to identify a low-dimensional subspace of policies that is robust to variations in model dynamics. Because this policy class is low-dimensional, we can learn an instance from this class on the real system using much less data than would be required to learn a policy in the original class. In our approach, we sample several models from a distribution over the ...
J. Zico Kolter, Andrew Y. Ng
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where RSS
Authors J. Zico Kolter, Andrew Y. Ng
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