  Active inference is a theory that underpins the way biological agent's
perceive and act in the real world. At its core, active inference is based on
the principle that the brain is an approximate Bayesian inference engine,
building an internal generative model to drive agents towards minimal surprise.
Although this theory has shown interesting results with grounding in cognitive
neuroscience, its application remains limited to simulations with small,
predefined sensor and state spaces.
  In this paper, we leverage recent advances in deep learning to build more
complex generative models that can work without a predefined states space.
State representations are learned end-to-end from real-world, high-dimensional
sensory data such as camera frames. We also show that these generative models
can be used to engage in active inference. To the best of our knowledge this is
the first application of deep active inference for a real-world robot
navigation task.
