Junming Fan
Papers
17
Total Citations
896
H-Index
14
About
Junming Fan is a leading researcher at the intersection of human-robot collaboration (HRC), computer vision, and artificial intelligence for smart manufacturing. His work centers on developing intelligent, human-centric systems that enable robots to understand, anticipate, and respond to human intentions in dynamic industrial environments — a critical challenge as manufacturing evolves toward Industry 5.0. Fan's most influential contributions include pioneering multimodal transfer-learning approaches for proactive action prediction in collaborative assembly (133 citations) and vision-based holistic scene understanding frameworks that allow robots to interpret complex human behaviors in real time (182 citations). His more recent research boldly integrates large language models (LLMs) and vision-language models into robotic navigation and task planning, with his LLM-based cobot navigation paper already accumulating 117 citations since 2024. He has also advanced human digital twin modeling, collaborative intelligence for managing HRC uncertainties, and human-in-the-loop robot learning — collectively positioning him as a thought leader in adaptive, flexible automation. With over 750 cumulative citations across his top publications and consistently high-impact output across 2021–2025, Fan's research is rapidly shaping how next-generation smart factories will harmonize human expertise with robotic precision. His work offers invaluable foundations for students and researchers pursuing intelligent manufacturing and human-aware robotics.
Research Focus
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Top Papers
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