Censi, Andrea and Nilsson, Adam and Murray, Richard M. (2012) Motion planning in observations space with learned diffeomorphism models. , Pasadena, CA. http://resolver.caltech.edu/CaltechCDSTR:2012.004
- Submitted Version
See Usage Policy.
Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechCDSTR:2012.004
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:||Report or Paper (Technical Report)|
|Group:||Control and Dynamical Systems Technical Reports|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Andrea Censi|
|Deposited On:||27 Sep 2012 23:45|
|Last Modified:||18 Mar 2015 22:53|
Repository Staff Only: item control page