Partially Observable Markov Decision Processes in Robotics A Survey

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-02-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
20
1-20
Series
IEEE Transactions on Robotics
Abstract
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research.
Description
Publisher Copyright: IEEE
Keywords
AI-based methods, autonomous agents, Markov processes, partially observable Markov decision process (POMDP), Planning, planning under uncertainty, Robot kinematics, Robot sensing systems, Robots, scheduling and coordination, Task analysis, Uncertainty
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Citation
Lauri , M , Hsu , D & Pajarinen , J 2023 , ' Partially Observable Markov Decision Processes in Robotics A Survey ' , IEEE Transactions on Robotics , vol. 39 , no. 1 , pp. 21-40 . https://doi.org/10.1109/TRO.2022.3200138