Xiaodong Hu
Papers
1
Total Citations
6
H-Index
1
About
Xiaodong Hu is a researcher in robotics and intelligent systems, with a focus on space robotics and machine learning applications for autonomous perception. His work addresses critical challenges in robotic manipulation and environment recognition, particularly in the context of space exploration. Hu’s most cited paper, “A Minimal Dataset Construction Method Based on Similar Training for Capture Position Recognition of Space Robot” (2018), introduces an innovative approach to reducing the data requirements for training recognition systems in space robotic tasks. By leveraging similarity-based training, this method enables efficient capture position recognition, which is essential for autonomous docking and debris removal missions. Despite its relatively modest citation count of 6, the work reflects a growing interest in data-efficient learning for high-stakes, resource-constrained environments. Hu’s contributions lie at the intersection of computer vision, robotics, and space systems, where he advances practical solutions for real-world deployment. His research is particularly relevant for students and engineers working on autonomous systems, offering a pathway to robust performance with minimal data—a key requirement for space applications where labeled datasets are scarce.
Research Focus
Key Achievements
Top Papers
- 1