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The robust balancing capability of humanoid robots against disturbances hasbeen considered as one of the crucial requirements for their practical mobilityin real-world environments. In particular, many studies have been devoted tothe efficient implementation of the three balance strategies, inspired by humanbalance strategies involving ankle, hip, and stepping strategies, to endowhumanoid robots with human-level balancing capability. In this paper, a robustbalance control framework for humanoid robots is proposed. Firstly, a novelModel Predictive Control (MPC) framework is proposed for Capture Point (CP)tracking control, enabling the integration of ankle, hip, and steppingstrategies within a single framework. Additionally, a variable weighting methodis introduced that adjusts the weighting parameters of the Centroidal AngularMomentum (CAM) damping control over the time horizon of MPC to improve thebalancing performance. Secondly, a hierarchical structure of the MPC and astepping controller was proposed, allowing for the step time optimization. Therobust balancing performance of the proposed method is validated throughextensive simulations and real robot experiments. Furthermore, a superiorbalancing performance is demonstrated, particularly in the presence ofdisturbances, compared to a state-of-the-art Quadratic Programming (QP)-basedCP controller that employs the ankle, hip, and stepping strategies. Thesupplementary video is available at https://youtu.be/CrD75UbYzdc

A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies