Mammals can generate autonomous behaviors in various complex environments through the coordination and interaction of activities at different levels of their central nervous system. In this paper, we propose a novel hierarchical learning control framework by mimicking the hierarchical structure of the central nervous system along with their coordination and interaction behaviors. The framework combines the active and passive control systems to improve both the flexibility and reliability of the control system as well as to achieve more diverse autonomous behaviors of robots. Specifically, the framework has a backbone of independent neural network controllers at different levels and takes a three-level dual descending pathway structure, inspired from the functionality of the cerebral cortex, cerebellum, and spinal cord. We comprehensively validated the proposed approach through the simulation as well as the experiment of a hexapod robot in various complex environments, including obstacle crossing and rapid recovery after partial damage. This study reveals the principle that governs the autonomous behavior in the central nervous system and demonstrates the effectiveness of the hierarchical control approach with the salient features of the hierarchical learning control architecture and combination of active and passive control systems.

翻译：哺乳动物能够通过中枢神经系统不同层级活动的协调与交互，在各种复杂环境中产生自主行为。本文通过模拟中枢神经系统的分层结构及其协调交互行为，提出了一种新颖的分层学习控制框架。该框架结合主动与被动控制系统，既提升了控制系统的灵活性与可靠性，又实现了机器人更为多样化的自主行为。具体而言，该框架以不同层级的独立神经网络控制器为骨干，并借鉴大脑皮层、小脑和脊髓的功能，采用三级双下行通路结构。我们通过仿真以及在六足机器人上进行的实验，全面验证了所提方法在多种复杂环境中的有效性，包括越障和局部损伤后的快速恢复。本研究揭示了中枢神经系统调控自主行为的原理，并展示了分层控制方法的有效性，其突出特点在于分层学习控制架构以及主动与被动控制系统的结合。