Tae Yong Kuc
Papers
2
Total Citations
20
H-Index
2
About
Tae Yong Kuc is a robotics researcher whose work spans intelligent control systems and autonomous robot behavior, with particular expertise in person-following technology and biped locomotion. His 2018 paper on Classification-Lock Tracking Strategy demonstrated a sophisticated approach to enabling robots to follow people reliably in complex indoor environments, leveraging computer vision to localize and track targets amid real-world challenges — work that has garnered 15 citations and represents a meaningful advance in human-robot interaction. Earlier in his career, Kuc tackled the demanding problem of biped walking robots, proposing in 2006 an intelligent controller that employs genetic algorithms to optimize the placement of mass centers across a robot's links, minimizing energy consumption while maintaining stable, repetitive gait — a contribution that reflects both his algorithmic creativity and his commitment to practical robotic efficiency. Across these contributions, Kuc demonstrates a consistent focus on making robots smarter and more adaptive, whether navigating crowded indoor spaces or mimicking the nuanced mechanics of human walking. His blend of machine learning, evolutionary computation, and computer vision positions him as a versatile contributor to the broader field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 2