Partially Observable Markov Decision Processes in Robotics A Survey

Mikko Lauri, David Hsu, Joni Pajarinen

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

21 Sitaatiot (Scopus)
165 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut21-40
Sivumäärä20
JulkaisuIEEE Transactions on Robotics
Vuosikerta39
Numero1
Varhainen verkossa julkaisun päivämäärä22 syysk. 2022
DOI - pysyväislinkit
TilaJulkaistu - 1 helmik. 2023
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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