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Adaptive Workspace Biasing for Sampling Based Planners
M. Zucker, J. Kuffner, and J. Bagnell
Proc. IEEE Int'l Conf. on Robotics and Automation, May, 2008.

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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.

Notes

Associated center: CFR
Associated lab/group: Planning and Autonomy Lab
Associated project: Learning Locomotion

Text Reference

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