  We present an end-to-end procedure for embodied exploration inspired by two
biological computations: predictive coding and uncertainty minimization. The
procedure can be applied to exploration settings in a task-independent and
intrinsically driven manner. We first demonstrate our approach in a maze
navigation task and show that it can discover the underlying transition
distributions and spatial features of the environment. Second, we apply our
model to a more complex active vision task, where an agent actively samples its
visual environment to gather information. We show that our model builds
unsupervised representations through exploration that allow it to efficiently
categorize visual scenes. We further show that using these representations for
downstream classification leads to superior data efficiency and learning speed
compared to other baselines while maintaining lower parameter complexity.
Finally, the modularity of our model allows us to probe its internal mechanisms
and analyze the interaction between perception and action during exploration.
