Papers
5
Total Citations
134
H-Index
4
About
Xingyu Li is a rising researcher at the intersection of intelligent manufacturing, human-robot collaboration, and sustainable production systems. His work spans deep learning applications in industrial quality control, motion recognition, and advanced sensor fusion — areas increasingly critical to the future of smart factories and autonomous systems. Li's most influential contribution to date is his 2021 work on early event detection in ultrasonic welding quality prediction, which has garnered 57 citations and demonstrates how deep learning can proactively identify manufacturing defects in real time. Closely following is his attention-based deep learning framework for inertial motion recognition in human-robot collaboration (53 citations), which advances how robots perceive and respond to human movement in shared workspaces. His 2024 visual-inertial fusion method further pushes these boundaries by addressing the persistent challenge of human pose estimation in occluded collaborative environments. Beyond perception and control, Li has broadened his scope to include sustainable manufacturing, contributing a comprehensive survey on machine learning applications in remanufacturing and circular economy strategies. His safety posture field framework for mobile manipulators reflects a commitment to safe, efficient human-robot interaction. Collectively, his work positions him as a versatile and impactful voice in intelligent, human-centered manufacturing research.
Research Focus
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Top Papers
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