Papers
3
Total Citations
113
H-Index
3
About
Sibang Liu is a robotics researcher whose work spans legged locomotion, mobile robot control, and computer vision. His most impactful contribution addresses a fundamental challenge in quadruped robotics: optimizing contact-force distribution under external disturbances. In his highly cited 2014 paper (97 citations), Liu formulated constrained dynamics for quadruped robots and derived a reduced-order dynamical model, then developed a hybrid control approach combining gradient-based optimization with adaptive neural networks to achieve stable, disturbance-resistant locomotion. This work has been foundational for researchers working on dynamic walking and force control in legged systems. Liu has also explored multi-task learning for face detection and recognition, demonstrating versatility across perception and control domains. More recently, he has contributed to wheeled mobile robot control, proposing a robust hybrid controller that integrates model predictive control with mixed H2/H∞ techniques to handle kinematic disturbances in path tracking. This work addresses practical challenges in autonomous navigation for non-holonomic platforms. Across his publications, Liu demonstrates a consistent focus on bridging theoretical control methods with real-world robotic systems, making his research valuable for both academic study and practical implementation in robotics.
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