Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

Online solvers for partially observable Markov decision processes have been applied to
problems with large discrete state spaces, but continuous state, action, and observation …

This paper introduces a knowledge-driven approach to real-time, continuous activity
recognition based on multisensor data streams in smart homes. The approach goes beyond …

In a spoken dialog system, determining which action a machine should take in a given
situation is a difficult problem because automatic speech recognition is unreliable and …

Partially observable Markov decision processes (POMDPs) form an attractive and principled
framework for agent planning under uncertainty. Point-based approximate techniques for …

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 …

This paper presents a real-time vision-based system to assist a person with dementia wash
their hands. The system uses only video inputs, and assistance is given as either verbal or …

We propose a novel approach to optimize Partially Observable Markov Decisions Processes
(POMDPs) defined on continuous spaces. To date, most algorithms for model-based …

We present a formal mathematical modeling framework for a multi-agent sequential decision
problem during an epidemic. The problem is formulated as a collaboration between a …

The partially observable Markov decision process (POMDP) provides a principled
mathematical model for integrating perception and planning, a major challenge in robotics …