Papers
1
Total Citations
17
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1
About
Yamin Han is a researcher at the forefront of robotic manipulation and intelligent perception, with a primary focus on efficient grasp detection for autonomous systems. His most-cited work, "DSC-GraspNet: A Lightweight Convolutional Neural Network for Robotic Grasp Detection" (2023, 17 citations), addresses a critical bottleneck in robotics: the trade-off between detection accuracy and computational speed. Han’s key contribution lies in designing a streamlined convolutional neural network that achieves robust grasp detection without the heavy computational overhead typical of existing methods. This innovation is particularly impactful for real-time applications, including virtual reality-based teleoperation, where low latency and reliability are paramount. By enabling robots to autonomously identify and grasp objects with greater efficiency, Han’s work advances the practical deployment of intelligent robotic systems in dynamic environments. His research underscores a commitment to bridging the gap between high-performance algorithms and real-world constraints, making him a notable figure in the field of robotic perception and control.
Research Focus
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Top Papers
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