Learning to guide task and motion planning using score-space representation
Name
1807.09962.pdf
Description
Submitted version
Size
6.1 MB
Format
Adobe PDF
Checksum (MD5)
708d95e23988f5fc00a0a97791af6d45
Author(s)
Kim, Beomjoon
Wang, Zi
Kaelbling, Leslie P
Lozano-Pérez, Tomás
Date Issued
May 2019
Journal
International Journal of Robotics Research
Publisher
SAGE Publications
Citation
Kim, Beomjoon et al. "Learning to guide task and motion planning using score-space representation." International Journal of Robotics Research 38, 7 (June 2019): 793-812 © 2019 The Author(s)
Version
Original manuscript
Abstract
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 the 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 performs 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/10.1177/0278364919848837
