  Active inference, a theoretical construct inspired by brain processing, is a
promising alternative to control artificial agents. However, current methods do
not yet scale to high-dimensional inputs in continuous control. Here we present
a novel active inference torque controller for industrial arms that maintains
the adaptive characteristics of previous proprioceptive approaches but also
enables large-scale multimodal integration (e.g., raw images). We extended our
previous mathematical formulation by including multimodal state representation
learning using a linearly coupled multimodal variational autoencoder. We
evaluated our model on a simulated 7DOF Franka Emika Panda robot arm and
compared its behavior with a previous active inference baseline and the Panda
built-in optimized controller. Results showed improved tracking and control in
goal-directed reaching due to the increased representation power, high
robustness to noise and adaptability in changes on the environmental conditions
and robot parameters without the need to relearn the generative models nor
parameters retuning.
