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Designing controllers to achieve natural motor capabilities for multi-jointrobots is a significant challenge. However, animals in nature are naturallywith basic motor abilities and can master various complex motor skills throughacquired learning. On the basis of analyzing the mechanism of the central motorsystem in mammals, we propose a novel pre-training reinforcement learningalgorithm that enables robots to learn rich motor skills and apply them tocomplex task environments without relying on external data. We first design askill based network similar to the cerebellum by utilizing the selectionmechanism of voluntary movements in the basal ganglia and the basic motorregulation ability of the cerebellum. Subsequently, by imitating the structureof advanced centers in the central motor system, we propose a high-level policyto generate different skill combinations, thereby enabling the robot to acquirenatural motor abilities. We conduct experiments on 4 types of robots and 22task environments, and the results show that the proposed method can enabledifferent types of robots to achieve flexible motor skills. Overall, ourresearch provides a promising framework for the design of neural network motorcontrollers.

A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control