用在线优化让四足机器人快速学会适应不同地形和负载的高效走路方式。
Online Optimization of Central Pattern Generators for Quadruped Locomotion
- 通过贝叶斯优化实时调整脊髓神经网络参数，实现在线自适应控制。
- 3分钟内完成优化，支持15公斤负载、10度坡度及摩擦系数变化下的稳定行走。
- 融合力反馈与虚拟模型控制，提升机器人稳定性与能耗效率，适合实际应用。
传统足式机器人运动控制器通常离线设计或训练，而动物出生即可行走并迅速通过少量真实交互提升运动能力。这种运动控制依赖于脊髓中的节律性神经网络——中央模式发生器（CPG）。CPG模型广泛用于机器人运动生成，但常需大量人工调参或使用遗传算法进行离线优化。本文提出一种基于贝叶斯优化的在线优化框架，实现对CPG参数的实时调整。实验表明，该框架可快速适应不同速度指令及环境变化，如摩擦系数、地形坡度和附加质量（最高达15公斤）。研究发现，将力反馈引入相位方程，并结合姿态控制（虚拟模型控制），显著提升机器人稳定性和能效。在Unitree Go1硬件平台上验证，可在3分钟内完成优化，实现在多种场景下对目标速度的高效适应。
原文摘要 · Abstract (English)
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
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