The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The widespread success of sampling-based planning 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 general 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 configuration space sampling. Experimental results show our algorithm to be effective across a variety of robotic platforms and high-dimensional configuration spaces.

Keywords

Motion and Path Planning, Learning and Adaptive Systems, Nonholonomic Motion Planning

Notes

Associated Center(s) / Consortia: Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Planning and Autonomy Lab

Associated Project(s): Learning Locomotion

Text Reference

Matthew Zucker, James Kuffner, and J. Andrew (Drew) Bagnell, "Adaptive Workspace Biasing for Sampling Based Planners," Proc. IEEE Int'l Conf. on Robotics and Automation, May, 2008.

BibTeX Reference

@inproceedings{Zucker_2008_5951,
   author = "Matthew Zucker and James Kuffner and J. Andrew (Drew) Bagnell",
   title = "Adaptive Workspace Biasing for Sampling Based Planners",
   booktitle = "Proc. IEEE Int'l Conf. on Robotics and Automation",
   month = "May",
   year = "2008",
}

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