Markov model

Key (lock)

Mathematics

Markov chain

Operating system

Statistics

Markov decision process

Machine learning

Engineering

Artificial intelligence

Markov process

Systems engineering

Partially observable Markov decision process

Computer science

Task (project management)

Process (computing)

Robotics

Computer security

Robot

IEEE Transactions on Robotics

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.

Partially Observable Markov Decision Processes in Robotics: A Survey