  The predictive processing (PP) hypothesizes that the predictive inference of
our sensorimotor system is encoded implicitly in the regularities between
perception and action. We propose a neural architecture in which such
regularities of active inference are encoded hierarchically. We further suggest
that this encoding emerges during the embodied learning process when the
appropriate action is selected to minimize the prediction error in perception.
Therefore, this predictive stream in the sensorimotor loop is generated in a
top-down manner. Specifically, it is constantly modulated by the motor actions
and is updated by the bottom-up prediction error signals. In this way, the
top-down prediction originally comes from the prior experience from both
perception and action representing the higher levels of this hierarchical
cognition. In our proposed embodied model, we extend the PredNet Network, a
hierarchical predictive coding network, with the motor action units implemented
by a multi-layer perceptron network (MLP) to modulate the network top-down
prediction. Two experiments, a minimalistic world experiment, and a mobile
robot experiment are conducted to evaluate the proposed model in a qualitative
way. In the neural representation, it can be observed that the causal inference
of predictive percept from motor actions can be also observed while the agent
is interacting with the environment.
