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Motion planning in observations space with learned diffeomorphism models
Abstract We consider the problem of planning motion …
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.
ke the search efficient in all directions.  +
Authors Andrea Censi, Adam Nilsson and Richard M. Murray  +
ID 2012l  +
Source Submitted, 2013 International Conference on Robotics and Automation (ICRA)  +
Tag cnm13-icra  +
Title Motion planning in observations space with learned diffeomorphism models +
Type Conference Paper  +
Categories Papers
Modification date
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15 May 2016 06:15:43  +
URL
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http://www.cds.caltech.edu/~murray/preprints/cnm13-icra_s.pdf  +
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Motion planning in observations space with learned diffeomorphism models + Title
 

 

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