Yi Man
Papers
3
Total Citations
46
H-Index
3
About
Yi Man is a leading researcher in robotic perception and manipulation, with a focus on enabling robots to operate reliably in complex, unstructured environments. His work addresses critical challenges in robot grasping and object handling, particularly for difficult-to-perceive items. Man’s major contributions include the development of the first RGB-D cross-background robot grasp detection dataset and a novel background-adaptive grasping network, which significantly improves robots’ adaptability to changing scenes—a pressing issue in modern manufacturing. His research on transparent object depth perception introduces orientation-aware guidance and texture enhancement techniques, solving the long-standing problem of depth inaccuracy for transparent materials. Additionally, Man’s collaborative weight assignment RGB-depth fusion strategy advances unknown object instance segmentation, crucial for autonomous robots in unstructured settings. With over 46 citations across his most prominent works since 2023, his impact is rapidly growing. His 2024 paper on cross-background grasping has already garnered 25 citations, underscoring its significance. Yi Man’s innovative approaches are paving the way for more flexible, intelligent robotic systems capable of handling real-world complexity.
Research Focus
Key Achievements
Top Papers
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