학술논문
Planning with Intermittent State Observability: Knowing When to Act Blind
Document Type
Conference
Source
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Intelligent Robots and Systems (IROS), 2022 IEEE/RSJ International Conference on. :11657-11664 Oct, 2022
Subject
Language
ISSN
2153-0866
Abstract
Contemporary planning models and methods often rely on constant availability of free state information at each step of execution. However, autonomous systems are increasingly deployed in the open world where state information may be costly or simply unavailable in certain situations. Failing to account for sensor limitations may lead to costly behavior or even catastrophic failure. While the partially observable Markov decision process (POMDP) can be used to model this problem, solving POMDPs is often intractable. We introduce a planning model called a semi-observable Markov decision process (SOMDP) specifically designed for MDPs where state observability may be intermittent. We propose an approach for solving SOMDPs that uses memory states to proactively plan for the potential loss of sensor information while exploiting the unique structure of SOMDPs. Our theoretical analysis and empirical evaluation demonstrate the advantages of SOMDPs relative to existing planning models.