Yuetong Xu
Papers
7
Total Citations
107
H-Index
5
About
Yuetong Xu is a leading researcher in intelligent robotics, specializing in deep learning for point cloud processing, robot grasping, and additive manufacturing defect detection. Their work bridges the gap between computer vision and robotic manipulation, enabling machines to perceive and interact with complex environments. Xu’s most-cited paper, "Grasping pose estimation for SCARA robot based on deep learning of point cloud" (42 citations), introduces a novel method for accurately estimating object poses from 3D data, significantly improving robotic grasping in unstructured settings. Another key contribution, "Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing" (23 citations), addresses quality control in Fused Deposition Modeling by leveraging self-feature comparison to identify printing defects, enhancing manufacturing reliability. Xu’s work on instance segmentation of point clouds from RGB-D sensors (21 citations) further advances scene understanding for robotics. With over 100 total citations, Xu’s research has practical implications for industrial automation, from deburring with force impedance control to online posture correction in assembly. Their innovative use of simulation and deep learning for training data generation (11 citations) exemplifies a forward-thinking approach to scalable robotic solutions.
Research Focus
Key Achievements
Top Papers
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- 4Simulation and deep learning on point clouds for robot grasping11 citations · 2021
- 5Compliance Control of Deburring Robots based on Force Impedance7 citations · 2020
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