  In this work, we build upon the Active Inference (AIF) and Predictive Coding
(PC) frameworks to propose a neural architecture comprising a generative model
for sensory prediction, and a distinct generative model for motor trajectories.
We highlight how sequences of sensory predictions can act as rails guiding
learning, control and online adaptation of motor trajectories. We furthermore
inquire the effects of bidirectional interactions between the motor and the
visual modules. The architecture is tested on the control of a simulated
robotic arm learning to reproduce handwritten letters.
