Typical legged locomotion controllers are designed or trained offline. This is in contrast to many animals, which are able to locomote at birth, and rapidly improve their locomotion skills with few real-world interactions. Such motor control is possible through oscillatory neural networks located in the spinal cord of vertebrates, known as Central Pattern Generators (CPGs). Models of the CPG have been widely used to generate locomotion skills in robotics, but can require extensive hand-tuning or offline optimization of inter-connected parameters with genetic algorithms. In this paper, we present a framework for the \textit{online} optimization of the CPG parameters through Bayesian Optimization. We show that our framework can rapidly optimize and adapt to varying velocity commands and changes in the terrain, for example to varying coefficients of friction, terrain slope angles, and added mass payloads placed on the robot. We study the effects of sensory feedback on the CPG, and find that both force feedback in the phase equations, as well as posture control (Virtual Model Control) are both beneficial for robot stability and energy efficiency. In hardware experiments on the Unitree Go1, we show rapid optimization (in under 3 minutes) and adaptation of energy-efficient gaits to varying target velocities in a variety of scenarios: varying coefficients of friction, added payloads up to 15 kg, and variable slopes up to 10 degrees. See demo at: https://youtu.be/4qq5leCI2AI

翻译：典型的足式运动控制器采用离线设计或训练方式。这与许多动物形成鲜明对比，它们出生后即能运动，并通过少量现实世界交互迅速提升运动技能。此类运动控制能力源于脊椎动物脊髓中被称为中枢模式发生器（CPGs）的振荡神经网络。CPG模型已广泛应用于机器人运动技能生成，但通常需要大量人工调试或采用遗传算法对互联参数进行离线优化。本文提出一种通过贝叶斯优化实现CPG参数\textit{在线}优化的框架。实验表明，该框架能快速优化并适应变化的速率指令与地形条件，例如不同的摩擦系数、地形坡度角以及机器人附加质量载荷。我们研究了感觉反馈对CPG的影响，发现相位方程中的力反馈与姿态控制（虚拟模型控制）均有助于提升机器人稳定性和能量效率。在Unitree Go1的硬件实验中，我们在多种场景下实现了能量高效步态的快速优化（3分钟内）与适应性调整：包括变化的摩擦系数、最高15公斤的附加载荷以及最高10度的可变坡度。演示视频见：https://youtu.be/4qq5leCI2AI