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Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. Using probabilistic sampling, point-based POMDP solvers have drastically(More)
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomous robots. Partially observable Markov decision processes (POMDPs) provide a principled general framework for such planning tasks and have been successfully applied to several moderately complex robotic tasks, including navigation, manipulation, and target(More)
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