Min-Chi Kao
Papers
2
Total Citations
35
H-Index
2
About
Min-Chi Kao’s research lies at the intersection of humanoid robotics, intelligent control, and human-robot interaction. Her most influential work introduces a biped gait learning algorithm that leverages an artificial bee colony optimization, dynamically adapting to environmental impact. This approach dramatically reduces the tedious manual tuning of gait parameters—a persistent bottleneck in humanoid locomotion—making stable walking more autonomous and efficient. Her second highly cited contribution focuses on designing an interaction system that enables intuitive communication between humans and humanoid robots through user-defined hand gestures, integrating hardware architecture, circuit design, and motion planning. Together, these works have earned over 35 citations, reflecting their practical significance in advancing robot autonomy and accessibility. Kao’s contributions are particularly notable for addressing real-world deployment challenges: her gait algorithm reduces time-consuming calibration, while her gesture interface lowers the barrier for non-expert users. For students and researchers in robotics, her work exemplifies how bio-inspired computation and thoughtful interface design can solve core problems in legged locomotion and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2