Young‐Seog Kim
Papers
5
Total Citations
27
H-Index
3
About
Young-Seog Kim is a robotics researcher whose work centers on humanoid and android robot locomotion, with particular expertise in motion capture integration, gait generation, and balance control for biped robotic systems. His research addresses one of the most challenging problems in robotics: enabling machines to move with the fluid, natural quality of human walking. Kim's most significant contributions involve bridging the gap between human motion data and robot implementation. His pioneering work on human-like gait generation for biped android robots, which has garnered 10 citations, introduced a novel strategy combining motion capture techniques with Zero Moment Point (ZMP) trajectory analysis — a critical metric for maintaining dynamic balance in walking robots. He further advanced the field by developing efficient walking pattern mapping algorithms that translate imperfect motion capture data onto humanoid platforms, recognizing the practical challenges of real-world data quality. His innovation extended to developing cost-effective dual video camera motion capture systems, making human-like gait design more accessible to the broader robotics community. Later work explored whole-body motion generation through nonlinear constrained optimization and sophisticated ZMP tracking control strategies using disturbance observers for navigating uneven terrain. Kim's cumulative contributions provide foundational tools for researchers working toward truly naturalistic humanoid robot movement.
Research Focus
Key Achievements
Top Papers
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