Learning to guide task and motion planning using score-space representation

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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.
Publisher
SAGE PUBLICATIONS LTD
Issue Date
2019-05
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, v.38, no.7, pp.793 - 812

ISSN
0278-3649
DOI
10.1177/0278364919848837
URI
http://hdl.handle.net/10203/277319
Appears in Collection
RIMS Journal Papers
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