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ICRA
2005
IEEE

Planning with Continuous Actions in Partially Observable Environments

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Planning with Continuous Actions in Partially Observable Environments
Abstract— We present a simple randomized POMDP algorithm for planning with continuous actions in partially observable environments. Our algorithm operates on a set of reachable belief points, sampled by letting the robot interact randomly with the environment. We perform value iteration steps, ensuring that in each step the value of all sampled belief points is improved. The idea here is that by sampling actions from a continuous action space we can quickly improve the value of all belief points in the set. We demonstrate the viability of our algorithm on two sets of experiments: one involving an active localization task and one concerning robot navigation in a perceptually aliased office environment.
Matthijs T. J. Spaan, Nikos A. Vlassis
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where ICRA
Authors Matthijs T. J. Spaan, Nikos A. Vlassis
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