  Active inference is a mathematical framework which originated in
computational neuroscience as a theory of how the brain implements action,
perception and learning. Recently, it has been shown to be a promising approach
to the problems of state-estimation and control under uncertainty, as well as a
foundation for the construction of goal-driven behaviours in robotics and
artificial agents in general. Here, we review the state-of-the-art theory and
implementations of active inference for state-estimation, control, planning and
learning; describing current achievements with a particular focus on robotics.
We showcase relevant experiments that illustrate its potential in terms of
adaptation, generalization and robustness. Furthermore, we connect this
approach with other frameworks and discuss its expected benefits and
challenges: a unified framework with functional biological plausibility using
variational Bayesian inference.
