Xuefeng Dong
Papers
2
Total Citations
17
H-Index
2
About
Xuefeng Dong is a robotics and computer vision researcher whose work focuses on enabling intelligent manipulation in complex, real-world environments. His primary research areas include robotic grasp detection, object recognition in cluttered scenes, and deep learning for industrial inspection. Dong’s major contribution is the development of a practical, multi-stage grasp detection method for the Kinova robot operating in stacked environments—a challenging scenario where objects are piled on top of one another. This work, published in 2022 and garnering 14 citations, addresses the critical problem of accurately localizing both objects and viable grasp points in such settings, moving beyond idealized single-object scenarios. More recently, Dong has advanced the field of automated inspection with a 2025 paper introducing a separable self-attention mechanism for foreign object detection in cloud server centers. This innovation leverages efficient attention-based architectures to improve detection accuracy in high-stakes infrastructure. While still early in his career, Dong’s contributions are notable for their direct applicability to industrial robotics and data center maintenance, bridging the gap between theoretical deep learning and practical, deployable solutions.
Research Focus
Key Achievements
Top Papers
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