Brain-inspired sensorimotor robotic platform learning in cerebellum-driven movement tasks through a cerebellar realistic model

Biologically inspired neural mechanisms, coupling internal models and adaptive modules, can be an effective way of constructing a control system that exhibits a human-like behaviour. A brain-inspired controller has been developed, embedding a cerebellum-like adaptive module based on neurophysiological plasticity mechanisms. It has been tested as controller of an ad-hoc developed neurorobot, integrating a 3 degrees of freedom serial robotic arm with a motion tracking system. The learning skills have been tried out, designing a vestibular-ocular reflex (VOR) protocol. One robot joint was used to get the desired head turn, while another joint displacement corresponded to the eye motion, which was controlled by the cerebellar model output, used as joint torque. Along task repetitions, the cerebellum was able to produce an anticipatory eye displacement, which accurately compensated the head turn in order to keep on fixing the environmental object. Multiple tests have been implemented, pairing different head turn with object motion. The gaze error and the cerebellum output were quantified. The VOR was accurately tuned thanks to the cerebellum plasticity. The next steps will include the activation of multiple plasticity sites evaluating the real platform behaviour in different sensorimotor tasks.