Papers
2
Total Citations
6
H-Index
2
About
Xin Zhan is a researcher at the forefront of intelligent systems, with key contributions spanning computer vision, human-robot interaction, and applied deep learning. Zhan’s work is distinguished by its focus on bridging the gap between raw sensor data and actionable machine understanding. In the domain of environmental intelligence, Zhan developed an enhanced YOLOv5-based model for garbage identification and classification, achieving improved detection accuracy through advanced data augmentation techniques—a critical step toward deploying autonomous sorting robots for real-world waste management challenges. This work has garnered 3 citations for its practical approach to a pressing societal problem. Simultaneously, Zhan has made significant strides in human-robot interaction with the introduction of HuBo-VLM, a unified vision-language model designed to translate natural language commands into robotic actions. By addressing the fundamental gap between human instructions and machine code, this end-to-end model enables robots to interpret visual cues and verbal directives simultaneously, paving the way for more intuitive and responsive collaborative robots. With 3 citations, HuBo-VLM represents a notable achievement in making robotic systems more accessible and effective. Xin Zhan’s research demonstrates a clear trajectory toward creating intelligent machines that can see, understand, and act in human-centric environments.
Research Focus
Key Achievements
Top Papers
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