The past decade has seen a significant breakthrough in research on solving partially
observable Markov decision processes (POMDPs). Where past solvers could not scale …

Motion planning in uncertain and dynamic environments is an essential capability for
autonomous robots. Partially observable Markov decision processes (POMDPs) provide a …

For reinforcement learning in environments in which an agent has access to a reliable state
signal, methods based on the Markov decision process (MDP) have had many successes. In …

We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …

Motion planning with imperfect state information is a crucial capability for autonomous robots
to operate reliably in uncertain and dynamic environments. Partially observable Markov …

(POMDPs) provide a principled mathematical framework for motion planning of autonomous
robots in uncertain and dynamic environments. They have been successfully applied to …

Point-based algorithms and RTDP-Bel are approximate methods for solving POMDPs that
replace the full updates of parallel value iteration by faster and more effective updates at …

Partially observable Markov decision processes (POMDPs) have been successfully applied
to various robot motion planning tasks under uncertainty. However, most existing POMDP …

In a mixed environment of autonomous driverless vehicles and human driven vehicles
operating on the same road, identifying intentions of human drivers and interacting with …

Robotic exploration tasks involve inherent uncertainty. They typically include navigating
through unknown terrain, searching for features that may or may not be present, and …