Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
October 3, 2025Open Access

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

View Full Paper
Ask AI
Bookmark
Share

Authors

AGAdrian B. GhansahSESergio EstebanAAAaron D. Ames

Discussion

Loading...

Member takes

Overview

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

Key Points

  • 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.

Cite This Study

Ghansah et al. (2025) studied this question.

synapsesocial.com/papers/68e02f46f0e39f13e7fa2bc4https://doi.org/10.48550/arxiv.2509.04722
View Full Paper
Ask AI
Bookmark
Share