Kai-Fan Lee

Papers

Total Citations

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About

Kai-Fan Lee is a robotics researcher whose work centers on humanoid robot locomotion, dynamic stability, and intelligent control systems. His most influential contribution is the development of a humanoid robot simulator that uses Particle Swarm Optimization (PSO) for gait learning, a method designed to reduce motor damage and enable safer, more convenient motion training for adult-sized humanoids. This work, published in 2013 and cited 16 times, has been foundational for researchers exploring evolutionary optimization in bipedal locomotion. Lee also advanced robot balance control through the design of a 3-degree-of-freedom dynamic balancing waist for the adult-sized humanoid robot David II, integrating fuzzy control to improve trunk stability. While this 2014 paper has garnered 3 citations, it represents a key step in making humanoid robots more agile and robust. Lee’s research bridges simulation and real-world deployment, offering practical solutions for gait optimization and postural control. His contributions are particularly relevant for students and engineers working on humanoid robotics, evolutionary algorithms, and adaptive control systems, providing a clear pathway from simulation-based learning to physical robot performance.