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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 MonteCarloValueIteration (MCVI) for continuous-state POMDPs. MCVI samples...