  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.
