Robust person guidance by using online POMDPs

Abstract

The paper considers a guiding task in which a robot has to guide a person towards a destination. A robust operation requires to consider uncertain models on the person motion and intentions, as well as noise and occlusions in the sensors employed for the task. Partially Observable Markov Decision Processes (POMDPs) are used to model the task. The paper describes an enhancement on online POMDP solvers that allow to apply them to larger problems. The algorithm is used to control the robot in real-time for the guiding application. Results in simulation illustrate the approach.