Huiming Li

Papers

Total Citations

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About

Huiming Li is a researcher at the forefront of intelligent robotics and computer vision, with a focused interest in integrating deep learning with industrial automation. His most notable contribution is the pioneering work "Research on Robotic Arm Based on YOLO" (2022), which bridges object detection algorithms with real-world robotic manipulation. In this study, Li developed custom datasets tailored to industrial environments and successfully integrated the YOLO algorithm into a robotic arm, enabling visually guided, real-time target capture. This work demonstrates a practical pathway for deploying AI-driven perception in manufacturing and logistics. While his citation count is currently modest, the foundational nature of his research positions it as a key reference for future advancements in vision-based robotic control. Li’s work is particularly relevant for students and engineers exploring the intersection of deep learning and robotics, offering a clear example of how state-of-the-art detection models can be adapted for precise, automated tasks in dynamic industrial settings.