In order to interact successfully in social situations, a robot must be able to observe others' actions and base its own behavior on its beliefs about their intentions. Many ...
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are ...
Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. S...
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensorplanning in the ...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...