Quanquan Shao
Papers
11
Total Citations
299
H-Index
6
About
Quanquan Shao is a leading researcher in robotic manipulation and autonomous grasping, whose work bridges the gap between perception and action in cluttered, unstructured environments. His primary research areas include trajectory planning, deep learning for grasp detection, and visuomotor control. Shao’s most influential contribution is his work on smooth, time-optimal S-curve trajectory planning for automated robots and machines, which has garnered over 205 citations and become a foundational reference in industrial robotics. He has also made significant strides in suction grasp region prediction using self-supervised learning, enabling robots to reliably pick objects in dense clutter—a notoriously difficult problem due to occlusion and pose variability. His development of GraspFusionNet, a two-stage multi-parameter grasp detection network, further advances robust picking in complex scenes. Shao’s research integrates deep convolutional networks with classical control methods, as seen in his work on image moment-based visual servoing and real-time pose estimation. With a total of over 300 citations across his publications, Shao’s contributions are shaping the next generation of intelligent, adaptive robotic systems for manufacturing and logistics.
Research Focus
Key Achievements
Top Papers
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- 3Object Detection-Based One-Shot Imitation Learning with an RGB-D Camera10 citations · 2020
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- 5Deep learning for picking point detection in dense cluster8 citations · 2017
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