Yunhan Li
Papers
3
Total Citations
9
H-Index
2
About
Yunhan Li is a researcher advancing human-robot interaction and intelligent manufacturing through innovative sensing and deep learning techniques. Their work centers on three key areas: gesture-based robot control, automated weld seam tracking, and AI-driven object recognition for robotic grasping. Li’s 2024 study on an interactive gesture control system for collaborative manipulators using the Leap Motion Controller—cited 4 times—introduced a flexible alternative to fixed-position gesture detection, enabling more intuitive and dynamic control of robotic arms. In 2025, Li proposed a method for automatic extraction and tracking of robot weld seam paths using line structured light, also garnering 4 citations; this work overcomes challenges like high reflectivity and uneven illumination to reliably extract 3D seam features, enhancing welding precision and efficiency. Additionally, Li developed a deep learning-based approach for object workpiece recognition and grasp detection, integrating YOLO-Net with feature fusion and attention mechanisms to improve detection accuracy under poor lighting. These contributions demonstrate Li’s impact in making robotic systems more adaptable, precise, and intelligent for real-world industrial applications.
Research Focus
Key Achievements
Top Papers
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