  Adaptation to external and internal changes is major for robotic systems in
uncertain environments. Here we present a novel multisensory active inference
torque controller for industrial arms that shows how prediction can be used to
resolve adaptation. Our controller, inspired by the predictive brain
hypothesis, improves the capabilities of current active inference approaches by
incorporating learning and multimodal integration of low and high-dimensional
sensor inputs (e.g., raw images) while simplifying the architecture. We
performed a systematic evaluation of our model on a 7DoF Franka Emika Panda
robot arm by comparing its behavior with previous active inference baselines
and classic controllers, analyzing both qualitatively and quantitatively
adaptation capabilities and control accuracy. Results showed improved control
accuracy in goal-directed reaching with high noise rejection due to multimodal
filtering, and adaptability to dynamical inertial changes, elasticity
constraints and human disturbances without the need to relearn the model nor
parameter retuning.
