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- W2214249 abstract "Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Monte Carlo Value Iteration (MCVI) for continuous-state POMDPs. MCVI samples both a robot’s state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and preliminary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty." @default.
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- W2214249 date "2010-01-01" @default.
- W2214249 modified "2023-10-18" @default.
- W2214249 title "Monte Carlo Value Iteration for Continuous-State POMDPs" @default.
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- W2214249 doi "https://doi.org/10.1007/978-3-642-17452-0_11" @default.
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