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The cerebellum plays a distinctive role within our motor control system toachieve fine and coordinated motions. While cerebellar lesions do not lead to acomplete loss of motor functions, both action and perception are severallyimpacted. Hence, it is assumed that the cerebellum uses an internal forwardmodel to provide anticipatory signals by learning from the error in sensorystates. In some studies, it was demonstrated that the learning process relieson the joint-space error. However, this may not exist. This work proposes anovel fully spiking neural system that relies on a forward predictive learningby means of a cellular cerebellar model. The forward model is learnt thanks tothe sensory feedback in task-space and it acts as a Smith predictor. The latterpredicts sensory corrections in input to a differential mapping spiking neuralnetwork during a visual servoing task of a robot arm manipulator. In thispaper, we promote the developed control system to achieve more accurate targetreaching actions and reduce the motion execution time for the robotic reachingtasks thanks to the cerebellar predictive capabilities.

Vision-Based Control for Robots by a Fully Spiking Neural System Relying on Cerebellar Predictive Learning