Planning under Uncertainty for Robotic Tasks with Mixed Observability

Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to sc...

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Bibliographic Details
Published in:The International journal of robotics research 2010-07, Vol.29 (8), p.1053-1068
Main Authors: Ong, Sylvie C. W., Shao Wei Png, Hsu, David, Wee Sun Lee
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Decision making
Dynamical systems
Dynamics
Markov analysis
Markov processes
Motion planning
Observability (systems)
Performance enhancement
Representations
Robot dynamics
Robotics
Robots
Task complexity
Tasks
Vision systems
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Summary:Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic systems often have mixed observability : even when a robot’s state is not fully observable, some components of the state may still be so. We use a factored model to represent separately the fully and partially observable components of a robot’s state and derive a compact lower-dimensional representation of its belief space. This factored representation can be combined with any point-based algorithm to compute approximate POMDP solutions. Experimental results show that on standard test problems, our approach improves the performance of a leading point-based POMDP algorithm by many times.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364910369861