Wenjie Xue
Papers
2
Total Citations
33
H-Index
2
About
Wenjie Xue is a roboticist whose research lies at the intersection of computer vision and robotic manipulation, with a particular focus on enabling robots to perceive and interact with objects in unstructured, real-world environments. Her work addresses two critical challenges in service robotics: semantic understanding of object categories and robust pose estimation for difficult objects. In her highly cited 2022 paper, “SKP: Semantic 3D Keypoint Detection for Category-Level Robotic Manipulation,” Xue introduced a novel framework that allows robots to grasp and manipulate objects within the same category—such as cups or bottles—despite variations in shape, size, and appearance, a key capability for applications in food service and hospitality. Building on this, her 2023 work, “6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization,” tackles the notoriously difficult problem of estimating the full 6D pose of objects lacking visual texture, using only standard RGB cameras and multi-view geometry. With over 30 citations across these two papers alone, Xue’s contributions are gaining recognition for their practical impact on making robots more perceptive and adaptable in dynamic, human-centric settings.
Research Focus
Key Achievements
Top Papers
- 1SKP: Semantic 3D Keypoint Detection for Category-Level Robotic Manipulation18 citations · 2022
- 2