Papers
7
Total Citations
282
H-Index
7
About
Leihui Li is a leading researcher at the intersection of collaborative robotics, 3D computer vision, and intelligent manufacturing, with a particular focus on advancing automation for small and medium-sized enterprises (SMEs). Li’s major contributions center on developing deep learning-based solutions for 3D point cloud processing, enabling mobile robot manipulators to perform precise object detection, localization, and automatic plug-in charging—work that has garnered 97 citations in a single 2021 paper. Li has also produced a highly cited tutorial review on point cloud registrations (53 citations), establishing a foundational resource for the field. A key innovation is the application of Physics-Informed Neural Networks (PINNs) to model collaborative robot dynamics and identify joint parameters during physical human-robot interaction (pHRI), with two 2023 papers each earning 47 citations. More recently, Li has pioneered automatic robot hand-eye calibration using learning-based 3D vision and robust point cloud quality assessment methods for enhanced robotic scanning. This body of work—totaling over 280 citations across top venues—positions Li as a pivotal figure in making collaborative robots safer, more autonomous, and practically deployable in dynamic industrial environments.
Research Focus
Key Achievements
Top Papers
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- 5Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision14 citations · 2024
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