Biological studies reveal that neural circuits located at the spinal cord called central pattern generator (CPG) oscillates and generates rhythmic signals, which are the underlying mechanism responsible for rhythmic locomotion behaviors of animals. Inspired by CPG's capability to naturally generate rhythmic patterns, researchers have attempted to create mathematical models of CPG and utilize them for the locomotion of legged robots. In this paper, we propose a network architecture that incorporates CPGs for rhythmic pattern generation and a multi-layer perceptron (MLP) network for sensory feedback. We also proposed a method that reformulates CPGs into a fully-differentiable stateless network, allowing CPGs and MLP to be jointly trained with gradient-based learning. The results show that our proposed method learned agile and dynamic locomotion policies which are capable of blind traversal over uneven terrain and resist external pushes. Simulation results also show that the learned policies are capable of self-modulating step frequency and step length to adapt to the locomotion velocity.

翻译：生物学研究表明，位于脊髓的神经回路（称为中枢模式发生器，即Central Pattern Generator, CPG）能够振荡并产生节律性信号，这是动物节律性运动行为的基础机制。受CPG自然生成节律模式能力的启发，研究者尝试构建CPG的数学模型并应用于腿式机器人运动。本文提出了一种融合CPG生成节律模式与多层感知机（MLP）处理感觉反馈的网络架构。同时提出一种方法，将CPG重新构建为完全可微分的无状态网络，使得CPG与MLP可通过基于梯度的学习进行联合训练。结果表明，该方法习得的敏捷动态运动策略能够实现盲态穿越不平坦地形并抵抗外部推力。仿真结果进一步显示，该策略可通过自主调节步频与步长适应运动速度变化。