The robust balancing capability of humanoid robots against disturbances has been considered as one of the crucial requirements for their practical mobility in real-world environments. In particular, many studies have been devoted to the efficient implementation of the three balance strategies, inspired by human balance strategies involving ankle, hip, and stepping strategies, to endow humanoid robots with human-level balancing capability. In this paper, a robust balance control framework for humanoid robots is proposed. Firstly, a novel Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum (CAM) damping control over the time horizon of MPC to improve the balancing performance. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through extensive simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated, particularly in the presence of disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based CP controller that employs the ankle, hip, and stepping strategies. The supplementary video is available at https://youtu.be/CrD75UbYzdc

翻译：类人机器人在面对外部扰动时的鲁棒平衡能力，被视为其在现实环境中实现实用化移动的关键要求之一。受人类平衡策略（涉及踝关节、髋关节和迈步）启发，现有研究致力于高效整合三种平衡策略，使类人机器人具备人类级别的平衡能力。本文提出一种用于类人机器人的鲁棒平衡控制框架。首先，提出一种面向捕获点（CP）跟踪控制的新型模型预测控制（MPC）框架，实现将踝关节、髋关节和迈步策略统一集成至单一框架中。此外，引入一种可变权重方法，通过动态调整MPC时域内质心角动量（CAM）阻尼控制的权重参数以提升平衡性能。其次，提出MPC与迈步控制器的分层结构，实现步态时长的优化。通过大量仿真与真实机器人实验验证了所提方法的鲁棒平衡性能。与采用踝关节、髋关节和迈步策略的先进二次规划（QP）型捕获点控制器相比，本方法在存在外部扰动时展现出更优的平衡性能。补充视频见 https://youtu.be/CrD75UbYzdc