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dc.contributor.authorKim, Beomjoon
dc.contributor.authorKaelbling, Leslie Pack
dc.contributor.authorLozano-Perez, Tomas
dc.description.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.en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.sourceMIT web domainen_US
dc.titleLearning to guide task and motion planning using score-space representationen_US
dc.identifier.citationKim, Beomjoon, Kaelbling, Leslie Pack and Lozano-Perez, Tomas. 2017. "Learning to guide task and motion planning using score-space representation."
dc.eprint.versionAuthor's final manuscripten_US
mit.metadata.statusAuthority Work and Publication Information Neededen_US

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