Policy-contingent abstraction for robust robot control

Abstract

This paper presents a scalable control algorithm that enables a deployed mobile robot system to make high-level decisions under full consideration of its probabilistic belief. Our approach is based on insights from the rich literature of hierarchical controllers and hierarchical MDPs. The resulting controller has been successfully deployed in a nursing facility near Pittsburgh, PA. To the best of our knowledge, this work is a unique instance of applying POMDPs to high-level robotic control problems.

Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)

References in corpus (2)

• Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
• Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes

Cited by in corpus (5)

• Finding Approximate POMDP solutions Through Belief Compression
• Efficient Planning under Uncertainty with Macro-actions
• Hierarchical POMDP Controller Optimization by Likelihood Maximization
• Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
• Les POMDP font de meilleurs hackers: Tenir compte de l'incertitude dans les tests de penetration