Partially Observable Markov Decision Processes in Robotics: A Survey
| Autor: | Mikko Lauri, David Hsu, Joni Pajarinen |
|---|---|
| Přispěvatelé: | University of Hamburg, National University of Singapore, Department of Electrical Engineering and Automation, Aalto-yliopisto, Aalto University |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Markov processes scheduling and coordination Uncertainty Systems and Control (eess.SY) Robot kinematics Electrical Engineering and Systems Science - Systems and Control Computer Science Applications Planning Computer Science - Robotics Artificial Intelligence (cs.AI) partially observable Markov decision process (POMDP) Control and Systems Engineering Robot sensing systems Task analysis planning under uncertainty FOS: Electrical engineering electronic engineering information engineering AI-based methods autonomous agents Electrical and Electronic Engineering Robots Robotics (cs.RO) |
| Zdroj: | IEEE Transactions on Robotics. 39:21-40 |
| ISSN: | 1941-0468 1552-3098 |
| DOI: | 10.1109/tro.2022.3200138 |
| Popis: | 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. 20 pages, 10 figures. Author version of paper. Accepted for publication in IEEE Transactions on Robotics |
| Databáze: | OpenAIRE |
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