  Designing controllers to achieve natural motor capabilities for multi-joint
robots is a significant challenge. However, animals in nature are naturally
with basic motor abilities and can master various complex motor skills through
acquired learning. On the basis of analyzing the mechanism of the central motor
system in mammals, we propose a novel pre-training reinforcement learning
algorithm that enables robots to learn rich motor skills and apply them to
complex task environments without relying on external data. We first design a
skill based network similar to the cerebellum by utilizing the selection
mechanism of voluntary movements in the basal ganglia and the basic motor
regulation ability of the cerebellum. Subsequently, by imitating the structure
of advanced centers in the central motor system, we propose a high-level policy
to generate different skill combinations, thereby enabling the robot to acquire
natural motor abilities. We conduct experiments on 4 types of robots and 22
task environments, and the results show that the proposed method can enable
different types of robots to achieve flexible motor skills. Overall, our
research provides a promising framework for the design of neural network motor
controllers.
