Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience
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kim-aaai19.pdf
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2.07 MB
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Author(s)
Kim, Beomjoon
Kaelbling, Leslie P
Lozano-Pérez, Tomás
Date Issued
July 2019
Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Citation
Kim, Beomjoon et al. "Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience." Proceedings of the AAAI Conference on Artificial Intelligence 33, 1 (July 2019): 8017-8024 © 2019 Association for the Advancement of Artificial Intelligence
Version
Author's final manuscript
Abstract
We propose an actor-critic algorithm that uses past planning experience to improve the efficiency of solving robot task-and-motion planning (TAMP) problems. TAMP planners search for goal-achieving sequences of high-level operator instances specified by both discrete and continuous parameters. Our algorithm learns a policy for selecting the continuous parameters during search, using a small training set generated from the search trees of previously solved instances. We also introduce a novel fixed-length vector representation for world states with varying numbers of objects with different shapes, based on a set of key robot configurations. We demonstrate experimentally that our method learns more efficiently from less data than standard reinforcementlearning approaches and that using a learned policy to guide a planner results in the improvement of planning efficiency.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1609/aaai.v33i01.33018017
