Papers
26
Total Citations
461
H-Index
11
About
Jianhua Su is a robotics researcher whose work sits at the intersection of precision assembly, robotic manipulation, and intelligent automation. He is best known for developing the concept of the "Attractive Region in Environment" (ARE), a groundbreaking framework that enables high-precision robotic tasks without relying on high-precision sensors — a contribution that has garnered over 140 citations and reshaped thinking around sensorless manipulation strategies. Building on this foundation, Su has made sustained contributions to peg-in-hole insertion problems, tackling increasingly complex variants including eccentric pegs, unfixed holes, and dual peg-in-hole assemblies — challenges central to real-world industrial manufacturing. His research extends into compliant motion control, where he combines passive and active compliance strategies within high-dimensional configuration spaces to achieve robust robotic assembly. Su has also advanced robotic grasping through vision-based caging approaches and explored the theoretical relationship between cage grasps and form-closure. More recently, his work has embraced learning-from-demonstration techniques and visual affordance detection using convolutional neural networks, reflecting a forward-looking integration of machine learning into manipulation. His 2023 work on visual tracking with time-delay compensation further demonstrates his responsiveness to real-world deployment challenges. Collectively, Su's research offers both theoretical rigor and practical impact for next-generation smart manufacturing systems.
Research Focus
Key Achievements
Top Papers
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- 3Robot Precision Assembly Combining With Passive and Active Compliant Motions41 citations · 2021
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