Zaixing He
Papers
7
Total Citations
125
H-Index
6
About
Zaixing He is a robotics and computer vision researcher whose work sits at the intersection of industrial automation, pose estimation, and robot perception. He is best known for his contributions to visual servoing and 6D pose measurement of textureless and reflective metal parts — a notoriously difficult problem in industrial robotics where conventional feature-based methods frequently fail. His 2018 paper on moment-based 2.5-D visual servoing (47 citations) addressed critical shortcomings of existing industrial visual servoing systems, while his earlier work on circular feature-based pose measurement (21 citations) tackled the specific challenge of grasping low-texture components like bearings and flanges. He has pioneered generative and deep learning approaches to pose estimation, including ContourPose (20 citations), which leverages contour information for monocular 6D pose estimation of reflective surfaces, and a generative feature-to-image framework (15 citations) that advances robustness for real-world manufacturing scenarios. Beyond perception, He has explored spoken language understanding for service robots and whole-body motion planning for mobile manipulation, demonstrating a broad commitment to enabling truly autonomous robotic systems. His cumulative contributions make him a notable voice in intelligent manufacturing and robot autonomy research.
Research Focus
Key Achievements
Top Papers
- 1Moment-Based 2.5-D Visual Servoing for Textureless Planar Part Grasping47 citations · 2018
- 2A circular feature-based pose measurement method for metal part grasping21 citations · 2017
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