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In this paper, we present a novel control framework to achieve robust pushrecovery on bipedal robots while locomoting. The key contribution is theunification of hybrid system models of locomotion with a reduced-order modelpredictive controller determining: foot placement, step timing, and anklecontrol. The proposed reduced-order model is an augmented Linear InvertedPendulum model with zero moment point coordinates; this is integrated within amodel predictive control framework for robust stabilization under externaldisturbances. By explicitly leveraging the hybrid dynamics of locomotion, ourapproach significantly improves stability and robustness across varying walkingheights, speeds, step durations, and is effective for both flat-footed and morecomplex multi-domain heel-to-toe walking patterns. The framework is validatedwith high-fidelity simulation on Cassie, a 3D underactuated robot, showcasingreal-time feasibility and substantially improved stability. The resultsdemonstrate the robustness of the proposed method in dynamic environments.

Robust Push Recovery on Bipedal Robots: Leveraging Multi-Domain Hybrid Systems with Reduced-Order Model Predictive Control