Partially Observable Markov Decision Processes in Shared Autonomy Applications: A Survey
Shaldambayeva, Shyrailym Zhumakhanova, Kamila Sandygulova, Anara Rubagotti, Matteo Shintemirov, Almas
Shaldambayeva, Shyrailym
Zhumakhanova, Kamila
Sandygulova, Anara
Rubagotti, Matteo
Shintemirov, Almas
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Department
Computer Vision
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Type
Journal article
Date
2025
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English
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Abstract
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.
Citation
S. Shaldambayeva, K. Zhumakhanova, A. Sandygulova, M. Rubagotti and A. Shintemirov, "Partially Observable Markov Decision Processes in Shared Autonomy Applications: A Survey," in IEEE Access, vol. 13, pp. 186937-186951, 2025, doi: 10.1109/ACCESS.2025.3625601
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IEEE Access
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Keywords
Partially observable Markov decision process, POMDP, shared autonomy, shared control
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IEEE