Xingfei Zhu
Papers
2
Total Citations
5
H-Index
2
About
Xingfei Zhu is a robotics researcher specializing in real-time visual perception and human-robot interaction. Their work focuses on bridging the gap between high-accuracy computer vision and the computational constraints of embedded robotic systems. Zhu’s major contributions include a hybrid approach to robotic visual navigation that integrates object detection with scene segmentation, achieving real-time performance without sacrificing reliability—a critical advancement for autonomous operation in dynamic environments. Additionally, they developed an optimized YOLO-based model for real-time hand keypoint detection, enabling robots to interpret human gestures for grasping tasks on resource-limited hardware. With early publications already garnering citations, Zhu’s research addresses a pressing bottleneck in robotics: the trade-off between accuracy and speed. Their work is foundational for deploying vision-guided robots in manufacturing, healthcare, and service industries, where immediate responsiveness is essential. By prioritizing efficiency alongside precision, Xingfei Zhu is shaping the next generation of agile, perceptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2