High-Level Robot Behavior Control Using POMDPs

Joelle Pineau and Sebastian Thrun

This paper describes a robot controller which uses probabilistic decision-making techniques at the highest-level of behavior control. The POMDP-based robot controller has the ability to incorporate noisy and partial sensor information, 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 accelerate planning. This POMDP controller is implemented and tested onboard a mobile robot in the context of an interactive service task. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide guidance and information to elderly residents with mild physical and cognitive disabilities.

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