October 3, 2025Open Access

Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots

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The proposed hierarchical control framework enhanced humanoid robot locomotion in various terrains.
The high-level step planner operates at 40 Hz, optimizing multiple locomotion parameters through nonlinear predictive control.
Simulation and hardware experiments confirmed a 36% increase in push recovery success due to adaptive step timing.
Integrating upper body control with torso dynamics further improved disturbance rejection during locomotion.

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This approach improves locomotion stability and versatility using reduced-order models and predictive control.

Ghansah et al. (2025) studied this question.

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