Partially Observable Markov Decision Processes in Shared Autonomy Applications: A Survey
Shaldambayeva S. Zhumakhanova K. Sandygulova A. Rubagotti M. Shintemirov A.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2025#13186937 - 186951 pp.
Shared autonomy consists of a collaborative effort between a human user and a robotic system having a shared goal, in which the human-controlled robot adapts its behaviour to provide assistive actions. For effective assistance, shared autonomy systems are required to infer human goals and/or cognitive states from their inputs. Partially observable Markov decision processes (POMDPs) offer a rich mathematical framework for formulating the above-mentioned planning problems under uncertainty. This paper presents a systematic review on the reported applications of POMDPs in the shared-autonomy context. Various aspects of the current scientific literature have been analysed to determine common shared-autonomy scenarios, implementation of human parameters in POMDPs and assessment methodologies. The effectiveness and limitations of POMDP approach for shared autonomy systems are also discussed.
Partially observable Markov decision process (POMDP) , shared autonomy , shared control
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Nazarbayev University, School of Engineering and Digital Sciences, Department of Robotics, Astana, 010000, Kazakhstan
Mohamed bin Zayed University of Artificial Intelligence, Department of Computer Vision, Abu Dhabi, United Arab Emirates
Nazarbayev University
Mohamed bin Zayed University of Artificial Intelligence
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026