Deep Active Inference for Autonomous Robot Navigation

Understand

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.