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Motion planning in observations space with learned diffeomorphism models

Censi, Andrea and Nilsson, Adam and Murray, Richard M. (2013) Motion planning in observations space with learned diffeomorphism models. In: 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 2860-2867. ISBN 978-1-4763-5643-5.

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We consider the problem of planning motions in observations space, based on learned models of the dynamics that associate to each action a diffeomorphism of the observations domain. For an arbitrary set of diffeomorphisms, this problem must be formulated as a generic search problem. We adapt established algorithms of the graph search family. In this scenario, node expansion is very costly, as each node in the graph is associated to an uncertain diffeomorphism and corresponding predicted observations. We describe several improvements that ameliorate performance: the introduction of better image similarities to use as heuristics; a method to reduce the number of expanded nodes by preliminarily identifying redundant plans; and a method to pre-compute composite actions that make the search efficient in all directions.

Item Type:Book Section
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URLURL TypeDescription ItemTechnical report
Censi, Andrea0000-0001-5162-0398
Murray, Richard M.0000-0002-5785-7481
Additional Information:© Copyright 2013 IEEE.
Record Number:CaltechAUTHORS:20140724-125512319
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Official Citation:Censi, A; Nilsson, A; Murray, R.M., "Motion planning in observations space with learned diffeomorphism models," Robotics and Automation (ICRA), 2013 IEEE International Conference on , vol., no., pp.2860,2867, 6-10 May 2013 doi: 10.1109/ICRA.2013.6630973
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47467
Deposited By: George Porter
Deposited On:24 Jul 2014 22:01
Last Modified:10 Nov 2021 17:40

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