arXiv 2507.024472025-07-03cs.RO

HAC-LOCO：在连续外部扰动下学习四足运动的层次化主动柔顺控制

HAC-LOCO: Learning Hierarchical Active Compliance Control for Quadruped Locomotion under Continuous External Disturbances

School of Aeronautics and Astronautics, Shenzhen campus of Sun Yat-sen University（航空学院，中山大学深圳校区）

Xiang Zhou, Xinyu Zhang, Qingrui Zhang

备注：: 8 pages, 7 Figures

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AI总结：

本文提出HAC-LOCO两阶段层次学习框架，通过力估计学习主动柔顺动作模块，使四足机器人在连续外部扰动下兼顾鲁棒性与柔顺性，并在仿真和实机实验中优于现有RL运动控制器。

摘要：

尽管四足控制近年来取得了显著成就，但在面对不可预见的外部扰动时，确保鲁棒且柔顺的运动仍然具有挑战性。现有方法优先考虑运动鲁棒性而忽视柔顺性，往往导致僵硬、高频的运动以及能量效率低下。因此，本文提出了一个两阶段层次化学习框架，该框架能够基于力估计学习对外部力扰动采取主动反应。在第一阶段，速度跟踪策略与自编码器一起训练，以提取历史本体感受特征。通过监督学习学习一个基于神经网络的估计器，该估计器基于本体感受测量来估计身体速度和外部力。在第二阶段，受阻抗控制启发的柔顺动作模块基于预训练的编码器和策略进行学习。该模块用于根据实时力估计主动调整速度命令以响应外部力。借助柔顺动作模块，四足机器人能够鲁棒地处理轻微扰动，同时对显著力适当地屈服，从而在鲁棒性和柔顺性之间取得平衡。仿真和真实世界实验表明，我们的方法在鲁棒性、能量效率和安全性方面具有优越性能。实验对比表明，我们的方法优于最先进的基于强化学习的运动控制器。消融研究展示了柔顺动作模块的关键作用。

英文摘要：

Despite recent remarkable achievements in quadruped control, it remains challenging to ensure robust and compliant locomotion in the presence of unforeseen external disturbances. Existing methods prioritize locomotion robustness over compliance, often leading to stiff, high-frequency motions, and energy inefficiency. This paper, therefore, presents a two-stage hierarchical learning framework that can learn to take active reactions to external force disturbances based on force estimation. In the first stage, a velocity-tracking policy is trained alongside an auto-encoder to distill historical proprioceptive features. A neural network-based estimator is learned through supervised learning, which estimates body velocity and external forces based on proprioceptive measurements. In the second stage, a compliance action module, inspired by impedance control, is learned based on the pre-trained encoder and policy. This module is employed to actively adjust velocity commands in response to external forces based on real-time force estimates. With the compliance action module, a quadruped robot can robustly handle minor disturbances while appropriately yielding to significant forces, thus striking a balance between robustness and compliance. Simulations and real-world experiments have demonstrated that our method has superior performance in terms of robustness, energy efficiency, and safety. Experiment comparison shows that our method outperforms the state-of-the-art RL-based locomotion controllers. Ablation studies are given to show the critical roles of the compliance action module.

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