Papers
14
Total Citations
241
H-Index
7
About
Xuebin Yue is a leading researcher in intelligent robotics and deep learning, with a primary focus on developing autonomous systems to address global labor shortages caused by aging populations and declining birth rates. His major contributions center on two key areas: object detection algorithms for empty-dish recycling robots and gait prediction for lower limb exoskeleton robots. Yue pioneered the YOLO-GD, YOLO-MSA, and YOLO-GG algorithms, which enable real-time, ultralightweight detection and grasping of dishes on resource-constrained edge devices, with his most cited work (YOLO-GD, 64 citations) setting a benchmark for robotic automation in food service. In rehabilitation robotics, he introduced a transformer-based neural network for gait prediction using plantar force and a humanoid sliding mode neural network controller, both achieving 27 citations each, advancing wearable exoskeleton technology for patients with lower extremity dysfunction. Yue’s research has accumulated over 230 total citations, with notable achievements including FPGA-based acceleration for deep learning inference and IoT-driven automatic model generation, demonstrating his impact on practical, deployable robotic solutions. His work is essential reading for students and researchers interested in applied AI, edge computing, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 2An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots36 citations · 2023
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- 8YOLO-GG: a slight object detection model for empty-dish recycling robot6 citations · 2022
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