6,941 research outputs found
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
A Bayesian framework for optimal motion planning with uncertainty
Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path- planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state
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