Papers
3
Total Citations
31
H-Index
3
About
Yun Long is a robotics and autonomous systems researcher whose work spans the intersection of machine learning, control theory, and physical system modeling. Best known for developing HybridNet — a pioneering framework that integrates model-based and data-driven approaches to predict the spatiotemporal evolution of complex dynamical systems — Long's research addresses one of the fundamental challenges facing autonomous robots: reliable interaction with uncertain physical environments. HybridNet, his most cited contribution with 25 citations, has proven influential in advancing how robotic systems learn and anticipate system dynamics with limited prior knowledge. Long's more recent work demonstrates a broadening of his expertise into precision robotics and medical applications. His investigations into variable admittance control for remote robotic ultrasound scanning tackle critical issues of force tracking and patient safety, while his contributions to macro-micro vision-integrated micromanipulation systems push boundaries in surgical and laboratory robotics, enabling more efficient and resilient control under microscopic conditions. Together, these efforts reflect a coherent research vision: building intelligent robotic systems capable of operating safely and autonomously across complex, real-world environments — from clinical settings to nanoscale manipulation tasks.
Research Focus
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