Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control frameworks often rely on a singular control policy that models both higher (supraspinal) and spinal cord functions. In this work, we build upon our previous research by introducing two distinct neural networks: one tasked with modulating the frequency and amplitude of CPGs to generate the basic locomotor rhythm (referred to as the spinal policy, SCP), and the other responsible for receiving environmental perception data and directly modulating the rhythmic output from the SCP to execute precise movements on challenging terrains (referred to as the descending modulation policy). This division of labor more closely mimics the hierarchical locomotor control systems observed in legged animals, thereby enhancing the robot's ability to navigate various uneven surfaces, including steps, high obstacles, and terrains with gaps. Additionally, we investigate the impact of sensorimotor delays within our framework, validating several biological assumptions about animal locomotion systems. Specifically, we demonstrate that spinal circuits play a crucial role in generating the basic locomotor rhythm, while descending pathways are essential for enabling appropriate gait modifications to accommodate uneven terrain. Notably, our findings also reveal that the multi-layered control inherent in animals exhibits remarkable robustness against time delays. Through these investigations, this paper contributes to a deeper understanding of the fundamental principles of interplay between spinal and supraspinal mechanisms in biological locomotion. It also supports the development of locomotion controllers in parallel to biological structures which are ...

翻译：动物具有穿越复杂地形的卓越能力，这得益于大脑、脊髓中枢模式发生器（CPGs）与肌肉骨骼系统之间多种通路的协同作用。传统的仿生控制框架通常采用单一控制策略来同时建模高级（上脊髓）和脊髓功能。本研究在前期工作基础上引入两种不同的神经网络：一种负责调节CPG的频率和幅度以生成基础运动节律（称为脊髓策略，SCP），另一种负责接收环境感知数据并直接调节SCP的节律输出，以在复杂地形上执行精确运动（称为下行调节策略）。这种分工更紧密地模拟了四足动物中观察到的层次化运动控制系统，从而增强了机器人穿越各种不平整表面（包括台阶、高障碍物和间隙地形）的能力。此外，我们研究了框架内感觉运动延迟的影响，验证了关于动物运动系统的若干生物学假设。具体而言，我们证明脊髓回路在生成基础运动节律中起关键作用，而下行通路对于实现适应不平整地形的适当步态调整至关重要。值得注意的是，我们的研究结果还揭示了动物固有的多层控制对时间延迟具有显著鲁棒性。通过这些研究，本文有助于更深入理解生物运动中脊髓与上脊髓机制相互作用的基本原理，同时支持开发与生物结构并行的运动控制器，这些控制器……