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• W2187191512 abstract "The widespread success of sampling-based plan- ning algorithms stems from their ability to rapidly discover the connectivity of a configuration space. Past research has found that non-uniform sampling in the configuration space can significantly outperform uniform sampling; one important strategy is to bias the sampling distribution based on features present in the underlying workspace. In this paper, we unite several previous approaches to workspace biasing into a gen- eral framework for automatically discovering useful sampling distributions. We present a novel algorithm, based on the REINFORCE family of stochastic policy gradient algorithms, which automatically discovers a locally-optimal weighting of workspace features to produce a distribution which performs well for a given class of sampling-based motion planning queries. We present as well a novel set of workspace features that our adaptive algorithm can leverage for improved configu- ration space sampling. Experimental results show our algorithm to be effective across a variety of robotic platforms and high- dimensional configuration spaces." @default.
• W2187191512 created "2016-06-24" @default.
• W2187191512 creator A5039289696 @default.
• W2187191512 date "2008-01-01" @default.
• W2187191512 modified "2023-09-27" @default.
• W2187191512 title "Adaptive workspace biasing for sampling-based planners Matt Zucker James Kuffner J. Andrew Bagnell" @default.
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• W2187191512 hasPublicationYear "2008" @default.
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