High-level robot behavior control using POMDPs

Abstract

This paper describes a robot controller which uses proba-bilistic decision-making techniques at the highest-level of behavior control. The POMDP-based robot controller has the ability to incorporate noisy and partial sensor informa-tion, and can arbitrate between information gathering and performance-related actions. The complexity of the robot control domain requires a POMDP model that is beyond the capability of current exact POMDP solvers, therefore we present a hierarchical variant of the POMDP model which exploits structure in the problem domain to accel-erate planning. This POMDP controller is implemented and tested onboard a mobile robot in the context of an in-teractive service task. During the course of experiments conducted in an assisted living facility, the robot success-fully demonstrated that it could autonomously provide guidance and information to elderly residents with mild physical and cognitive disabilities.