四足运动中枢模式发生器的在线优化
Online Optimization of Central Pattern Generators for Quadruped Locomotion
AI总结：
本文提出基于贝叶斯优化的CPG参数在线优化框架，使四足机器人能在3分钟内快速适应速度指令、摩擦系数、坡度及负载变化，并通过力反馈与虚拟模型控制提升稳定性和能效。
摘要：
典型的足式运动控制器是离线设计或训练的。这与许多动物形成对比，动物在出生时就能运动，并通过少量真实世界交互迅速提升运动技能。这种运动控制通过位于脊椎动物脊髓中的振荡神经网络实现，称为中枢模式发生器（Central Pattern Generators, CPGs）。CPG模型已被广泛用于在机器人中生成运动技能，但可能需要大量手工调参或使用遗传算法对相互关联的参数进行离线优化。本文提出了一种通过贝叶斯优化对CPG参数进行在线优化的框架。我们表明，该框架能够快速优化并适应变化的速度指令和地形变化，例如变化的摩擦系数、地形坡度角以及放置在机器人上的附加质量负载。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以及姿态控制（虚拟模型控制，Virtual Model Control）均有利于机器人稳定性和能量效率。在Unitree Go1上的硬件实验中，我们展示了在多种场景下对节能步态的快速优化（不到3分钟）和适应不同目标速度的能力：变化的摩擦系数、高达15 kg的附加负载以及高达10度的可变坡度。演示见：https://youtu.be/4qq5leCI2AI
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
