]> 2026-07-12T10:58:19+00:00 Motion planning in observations space with learned diffeomorphism models 0 en 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. Andrea Censi, Adam Nilsson and Richard M. Murray 2012l Submitted, 2013 International Conference on Robotics and Automation (ICRA) cnm13-icra Conference Paper 2016-05-15T06:15:43Z 2457523.7609144 Motion planning in observations space with learned diffeomorphism models Title 102 en Title