Ya-Fang Ho
Papers
6
Total Citations
98
H-Index
5
About
Ya-Fang Ho is a leading researcher in humanoid robotics, specializing in gait learning, posture calibration, and bio-inspired optimization algorithms for bipedal locomotion. Her work addresses the fundamental challenge of enabling robots to walk naturally and stably, with a focus on reducing the tedious manual tuning of parameters. Ho’s most influential contributions include the development of an artificial bee colony algorithm for biped gait learning (27 citations) and a particle swarm optimization (PSO)-based method for intelligent posture calibration of robot arms (27 citations). She also advanced the field with a double-link linear inverted pendulum model (LIPM) for natural walking reference generation (19 citations) and created a humanoid robot simulator for safe, efficient gait training using PSO (16 citations). Notably, her work extends beyond locomotion to cognitive robotics, as seen in her exploration of human-like thinking in robots for tasks like ball throwing. Ho’s research has directly improved the stability and autonomy of adult-sized humanoid robots, including the David II platform, through innovations like a 3-DOF dynamic balancing waist with fuzzy control. With over 90 total citations, her contributions are essential reading for students and engineers advancing humanoid robot mobility and intelligent control.
Research Focus
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Top Papers
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