Designing controllers to achieve natural motion 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 neuro-inspired hierarchical 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 skills network similar to the cerebellum by utilizing the selection mechanism of voluntary movements in the basal ganglia and the regulatory ability of the cerebellum to regulate movement. Subsequently, by imitating the structure of advanced centers in the motion 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 motion skills. Overall, our research provides a promising framework for the design of robotic neural motor controllers.

翻译：设计控制器以实现多关节机器人的自然运动能力是一项重大挑战。然而，自然界中的动物天生具备基本运动能力，并能通过后天学习掌握各种复杂运动技能。在分析哺乳动物中枢运动系统机制的基础上，我们提出了一种受神经启发的分层强化学习算法，使机器人无需依赖外部数据即可学习丰富的运动技能，并将其应用于复杂任务环境。我们首先通过模拟基底节中的自主运动选择机制和小脑对运动的调节能力，设计了一个类似小脑的技能网络。随后，通过模仿运动系统中高级中枢的结构，我们提出了一种高层策略来生成不同的技能组合，从而使机器人获得自然的运动能力。我们在4种机器人类型和22个任务环境中进行了实验，结果表明，所提出的方法能够使不同类型的机器人实现灵活的运动技能。总体而言，我们的研究为机器人神经运动控制器的设计提供了一个有前景的框架。