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A distributed cerebellar-inspired learning model for robotic arm control.

The cerebellum plays a big role in motor control and motor coordination in mammals, especially for limbs control. Therefore, many cerebellar models were proposed to be applied in the field of robotic arm control. However, some problems exist in the current cerebellar modeling approach, such as lack of the expression of bio-characteristics, limited learning ability et al. Therefore, a distributed cerebellar-inspired learning model was proposed to mimic the physiology and anatomy features of the cerebellum. Meanwhile, this model could learn to adjust the motor command according to the error information provided by the inferior olive to achieve control goal. To test the performance of the cerebellar model, a robotic arm control system was implemented. The results showed that our model was able to complete the robotic arm control tasks successfully.