Papers
15
Total Citations
137
H-Index
7
About
Hyonyoung Han is a robotics and human-robot interaction researcher whose work spans computer vision, tactile sensing, muscle-computer interfaces, and intelligent manufacturing systems. Han's early contributions focused on piezoelectric-based muscle stiffness sensors capable of detecting muscle contraction and fatigue in real time — foundational work for physical human-robot interaction (pHRI) that has accumulated nearly 50 citations across multiple studies. This research established novel alternatives to traditional electromyography for motion intention detection in assistive and collaborative robotics. Han's later work pivoted toward robot perception and autonomous manipulation. A standout contribution is an HSV color-space-based object localization algorithm (2021, 41 citations) enabling robots to identify and grasp objects without prior knowledge or human intervention — a critical advance for flexible, personalized manufacturing. Han also contributed to the IROS 2019 Lifelong Robotic Vision Challenge, addressing continual learning in robotic object recognition. More recent efforts explore sim-to-real tactile transfer for in-hand object classification and deep reinforcement learning for stable grasping in cluttered environments. Across smart factory architecture and multi-agent 3D printing platforms, Han's body of work consistently bridges sensing, perception, and intelligent automation toward adaptive, human-centered robotic systems.
Research Focus
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