Partially Observable Markov Decision Processes in Robotics: A Survey

作     者：Lauri, Mikko Hsu, David Pajarinen, Joni 

作者机构：Department of Informatics Universität Hamburg Germany Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Automation Aalto University Finland Intelligent Autonomous Systems Technische Universität Darmstadt Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Surveys 

摘      要：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. Copyright © 2022, The Authors. All rights reserved.