Hierarchical learning control for autonomous robots inspired by central nervous system
This work addresses the challenge of autonomous robot control in complex scenarios, such as obstacle crossing and damage recovery, though it appears incremental by combining existing neural network controllers in a novel hierarchical structure.
The paper tackled the problem of enabling autonomous robot behaviors in complex environments by proposing a hierarchical learning control framework inspired by the central nervous system, achieving improved flexibility and reliability in simulations and experiments with a hexapod robot.
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.