Han-Pang Huang
Papers
2
Total Citations
23
H-Index
2
About
Han-Pang Huang is a leading figure in intelligent robotics and human motion modeling, with a career dedicated to bridging the gap between predictive algorithms and real-world robotic autonomy. His research focuses on pedestrian motion prediction, humanoid locomotion, and optimal control strategies. In a seminal 2008 work, Huang introduced a goal-directed pedestrian model that uses navigation functions and statistical human motion data for long-term path prediction. This computationally efficient model significantly outperformed existing approaches by avoiding failures in complex environments, earning 13 citations and influencing subsequent work in robot motion planning. Huang further advanced the field of humanoid robotics with his 2013 study on state-incremental optimal control for 3D center-of-gravity pattern generation. By addressing the limitations of neural networks and genetic algorithms in zero moment point control, his method provided a more robust framework for stable bipedal walking. With a career marked by innovative contributions to autonomous systems, Huang’s work continues to shape how robots perceive, predict, and interact with dynamic human environments.
Research Focus
Key Achievements
Top Papers
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