Qualitative analysis of POMDPs with temporal logic specifications for robotics applications

We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as...

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Bibliographic Details
Main Authors: Chatterjee, Krishnendu, Chmelik, Martin, Gupta, Raghav, Kanodia, Ayush
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Fuels
Probabilistic logic
Robots
Rocks
Synchronization
Uncertainty
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Summary:We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as parity objectives. We study the qualitative analysis problem for POMDPs with parity objectives that asks whether there is a controller (policy) to ensure that the objective holds with probability 1 (almost-surely). While the qualitative analysis of POMDPs with parity objectives is undecidable, recent results show that when restricted to finite-memory policies the problem is EXPTIME-complete. While the problem is intractable in theory, we present a practical approach to solve the qualitative analysis problem. We designed several heuristics to deal with the exponential complexity, and have used our implementation on a number of well-known POMDP examples for robotics applications. Our results provide the first practical approach to solve the qualitative analysis of robot motion planning with LTL properties in the presence of uncertainty.
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2015.7139019