Sample-Based Methods for Factored Task and Motion Planning
Author(s)
Garrett, Caelan; Lozano-Perez, Tomas; Kaelbling, Leslie
DownloadAccepted version (962.1Kb)
Open Access Policy
Open Access Policy
Creative Commons Attribution-Noncommercial-Share Alike
Terms of use
Metadata
Show full item recordAbstract
© 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.
Date issued
2017-07-12Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryPublisher
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