Dynamic belief state representations

•The utility of maintaining a dynamical belief state as opposed to a single state vector is introduced.•Canonical algorithms for computing and tracking belief states in robotics are reviewed.•A navigation example is used to illustrate correlations between belief state components.•Potential connections to work in psychophysics and neural representations are shown.

Perceptual and control systems are tasked with the challenge of accurately and efficiently estimating the dynamic states of objects in the environment. To properly account for uncertainty, it is necessary to maintain a dynamical belief state representation rather than a single state vector. In this review, canonical algorithms for computing and updating belief states in robotic applications are delineated, and connections to biological systems are highlighted. A navigation example is used to illustrate the importance of properly accounting for correlations between belief state components, and to motivate the need for further investigations in psychophysics and neurobiology.