Yeyubei Zhang
Papers
2
Total Citations
16
H-Index
2
About
Yeyubei Zhang is an emerging researcher at the intersection of robotics, artificial intelligence, and autonomous systems. Their work centers on applying advanced machine learning techniques to solve real-world challenges in robotic intelligence, with a particular focus on operational efficiency and environmental perception. Zhang's most influential contribution, "Warehouse Robot Task Scheduling Based on Reinforcement Learning to Maximize Operational Efficiency" (2025), has already garnered 12 citations, demonstrating rapid recognition within the robotics and logistics automation community. This work leverages reinforcement learning alongside cloud computing to push beyond existing limitations in robotic intelligence, offering meaningful advances in warehouse automation — a domain of growing industrial significance. Complementing this, Zhang's research on deep learning-based multi-modal fusion addresses one of autonomous navigation's most persistent challenges: robust perception in complex, unpredictable environments. By developing innovative feature extraction modules and adaptive fusion strategies, this work (4 citations) contributes practical architectural solutions for safer and more capable autonomous robots. Though early in their career, Zhang's research portfolio reflects a coherent and timely vision — bridging foundational machine learning with applied robotics. Students exploring autonomous systems, warehouse automation, or multi-modal AI will find Zhang's work both technically rigorous and practically motivated.
Research Focus
Key Achievements
Top Papers
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