  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.
