Sehoon Yea
Papers
2
Total Citations
8
H-Index
2
About
Sehoon Yea’s research lies at the intersection of robotics, human skill acquisition, and multi-manipulator coordination. His foundational work on extracting human expertise for robotic control introduced a novel framework using Hidden Markov Models to decode and replicate impedance parameters from human teaching data—a critical step toward making robots more adaptive and intuitive. This approach, detailed in his 1998 paper, has garnered 5 citations and remains a touchstone for researchers exploring skill transfer in human-robot interaction. Yea further advanced the field by tackling the complex challenge of coordinated motion between two robotic arms. His 2002 study on task-oriented path planning provided a rigorous mathematical resolution to motion redundancy, enabling smoother, more efficient collaboration between manipulators working on a shared workpiece. Though his citation counts are modest, Yea’s contributions are notable for their precision and foresight, laying groundwork for modern cooperative robotics and skill-based control systems. His work continues to inspire students and engineers seeking to bridge human dexterity with robotic precision.
Research Focus
Key Achievements
Top Papers
- 1Acquisition of the Human Skill with Hidden Markov Model5 citations · 1998
- 2