  Active inference (AI) is a persuasive theoretical framework from
computational neuroscience that seeks to describe action and perception as
inference-based computation. However, this framework has yet to provide
practical sensorimotor control algorithms that are competitive with alternative
approaches. In this work, we frame active inference through the lens of control
as inference (CaI), a body of work that presents trajectory optimization as
inference. From the wider view of `probabilistic numerics', CaI offers
principled, numerically robust optimal control solvers that provide uncertainty
quantification, and can scale to nonlinear problems with approximate inference.
We show that AI may be framed as partially-observed CaI when the cost function
is defined specifically in the observation states.
