Papers
1
Total Citations
3
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1
About
Hao Nan is a researcher at the forefront of robotic perception and intelligent manipulation, with a primary focus on advancing RGB-D-based grasping technologies. His most cited work, "ADFNet: A two-branch robotic grasping network based on attention mechanism" (2021), directly addresses a critical challenge in the field: effectively fusing complementary heterogeneous data from color and depth sensors while maintaining real-time performance. By proposing a novel two-branch architecture enhanced with attention mechanisms, Nan’s research enables robots to more accurately and efficiently identify and grasp objects in complex environments. This contribution has garnered 3 citations, establishing a foundation for further exploration in sensor fusion and deep learning for robotics. His work is particularly notable for its practical implications in industrial automation and service robotics, where reliable grasping is essential. Through ADFNet, Hao Nan demonstrates a commitment to bridging the gap between algorithmic innovation and real-world robotic applications, making his research a valuable reference for students and engineers seeking to improve autonomous manipulation systems.
Research Focus
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Top Papers
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