标题： HAC-LOCO：在持续外部干扰下用于四足运动的分层主动顺应控制学习 显示英文标题

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

摘要： 尽管在四足控制方面取得了显著的进展，但在存在不可预见的外部干扰的情况下，确保鲁棒且柔性的运动仍然具有挑战性。 现有方法优先考虑运动的鲁棒性而非柔性，通常会导致刚性、高频的运动以及能量效率低下。 因此，本文提出了一种两阶段的分层学习框架，该框架可以根据力估计主动对外部力干扰做出反应。 在第一阶段，速度跟踪策略与自编码器一起训练，以提炼历史本体感觉特征。 通过监督学习，学习了一个基于神经网络的估计器，该估计器根据本体感觉测量值估计身体速度和外部力。 在第二阶段，基于预训练的编码器和策略，学习了一个柔性动作模块，该模块受到阻抗控制的启发。 该模块用于根据实时力估计主动调整速度指令以应对外部力。 通过柔性动作模块，四足机器人可以在稳健处理微小干扰的同时适当让位于较大的力，从而在稳健性和柔性之间取得平衡。 仿真和真实世界实验表明，我们的方法在鲁棒性、能耗效率和安全性方面表现出色。 实验比较显示，我们的方法优于最先进的基于强化学习的运动控制器。 消融研究展示了柔性动作模块的关键作用。 显示英文摘要

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.