Sample-Based Methods for Factored Task and Motion Planning
Name
garrett-rss17.pdf
Description
Accepted version
Size
962.11 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Garrett, Caelan
Lozano-Perez, Tomas
Kaelbling, Leslie
Date Issued
July 12, 2017
Publisher
Robotics: Science and Systems Foundation
Citation
Garrett, Caelan, Lozano-Perez, Tomas and Kaelbling, Leslie. 2017. "Sample-Based Methods for Factored Task and Motion Planning."
Version
Author's final manuscript
Abstract
© 2017 MIT Press Journals. All rights reserved. There has been a great deal of progress in developing probabilistically complete methods that move beyond motion planning to multi-modal problems including various forms of task planning. This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and action spaces. The formulation characterizes conditions on the submanifolds in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that are provided as part of a domain specification. We present domain-independent sample-based planning algorithms and show that they are both probabilistically complete and computationally efficient on a set of challenging benchmark problems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.15607/rss.2017.xiii.039
