Learning to guide task and motion planning using score-space representation
Name
kim-icra17.pdf
Description
Accepted version
Size
5.41 MB
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Unknown
Checksum (MD5)
d36ba2dece73fb54db511a7076bb90bf
Author(s)
Kim, Beomjoon
Kaelbling, Leslie P
Lozano-Perez, Tomas
Date Issued
May 2017
Publisher
IEEE
Citation
Kim, Beomjoon, Kaelbling, Leslie Pack and Lozano-Perez, Tomas. 2017. "Learning to guide task and motion planning using score-space representation."
Version
Author's final manuscript
Abstract
© 2017 IEEE. In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score space, where we represent a problem instance in terms of performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach perform orders of magnitudes faster than an unguided planner.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2017.7989327
