Papers
8
Total Citations
178
H-Index
6
About
Jun Li is a versatile researcher whose work spans industrial economics, intelligent robotics, and computer vision-based quality inspection. His most impactful contribution, "Industrial Robots and Firm Productivity" (2023, 107 citations), examines the economic implications of automation, bridging engineering and applied economics to illuminate how robotic adoption reshapes industrial performance. His earlier foundational work in mobile robotics explored adaptive neural network architectures — particularly Radial Basis Function (RBF) networks — to enable robots to learn reactive behaviors through integrated unsupervised, supervised, and reinforcement learning paradigms, establishing a coherent framework for autonomous sensorimotor mapping across several publications from 2004 to 2008. Li further extended his robotics expertise into human-robot interaction, developing a touch-screen-based system for teaching calligraphy to robots, demonstrating sensitivity to cultural heritage preservation through technology. More recently, his work on SRPCNet (2024) addresses real-world industrial inspection challenges, proposing a self-reinforcing perception coordination network for detecting internal surface defects in seamless steel pipes — a practically significant advance in automated quality control. Collectively, Li's research reflects a career dedicated to making intelligent systems more adaptive, precise, and industrially relevant, with cumulative citations underscoring his growing influence across multiple disciplines.
Research Focus
Key Achievements
Top Papers
- 1Industrial robots and firm productivity107 citations · 2023
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- 4Towards online learning of reactive behaviors in mobile robotics11 citations · 2004
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- 6Teaching a calligraphy robot via a touch screen8 citations · 2014
- 7Growing RBF networks for learning reactive behaviours in mobile robotics4 citations · 2006
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