About

Ligang Ge is a leading researcher in bipedal and humanoid robot locomotion, with a focus on achieving robust, adaptive, and safe movement in dynamic environments. His major contributions center on developing advanced control frameworks that enable robots to maintain balance under large external disturbances. Ge’s most influential work, “Robust Locomotion Exploiting Multiple Balance Strategies: An Observer-Based Cascaded Model Predictive Control Approach” (2022, 23 citations), introduces a novel MPC approach that allows humanoid robots to dynamically exploit ankle, stepping, hip, and height strategies for balance recovery. He has further advanced the field with “Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning” (2023, 13 citations), which addresses passive safety in three-dimensional walking. His recent work on legged odometry (2024, 8 citations) tackles the critical challenge of position estimation for humanoid robots by fusing leg kinematics with IMU data. Ge’s research portfolio also includes reactive planning and control frameworks for disturbance rejection, whole-body controllers for running gaits, and compliance control for quadruped robots, demonstrating his broad expertise across legged locomotion systems.

Research Focus

Key Achievements

4
H-Index
6
Papers
54
Total Citations
9
Avg Citations/Paper
🏆 Most Cited Paper
Robust Locomotion Exploiting Multiple Balance Strategies: An Observer-Based Cascaded Model Predictive Control Approach
23 citations · 2022
📈 Most Prolific Year: 2023 (2 Papers)
🤝 Key Collaborators: 21
🏛 Institutions: Shenzhen Academy of Robotics, Omnitech Robotics (United States), National University of Defense Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago