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Carnegie Mellon Robotics Institute

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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.

Notes

Text Reference

Matthew Zucker, James Kuffner, and J. Andrew (Drew) Bagnell, "Adaptive workspace biasing for sampling-based planners," April, 2008.

BibTeX Reference

@inproceedings{Zucker_2008_7031,
   author = "Matthew Zucker and James Kuffner and J. Andrew (Drew) Bagnell",
   title = "Adaptive workspace biasing for sampling-based planners",
   booktitle = "",
   month = "April",
   year = "2008",
   number= "CMU-RI-TR-",
}

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