Papers

4

Total Citations

62

H-Index

3

About

Yu Zhai is a leading researcher in autonomous mobile robotics, specializing in deep reinforcement learning and perception for navigation in complex, dynamic environments. His work bridges the gap between theoretical AI and practical robotic systems, with a particular focus on enabling robots to operate safely and efficiently in crowded, unstructured spaces. Zhai’s most influential contribution is the development of graph relational reinforcement learning for large-scale crowd navigation, a paradigm-shifting approach that allows robots to model social interactions and predict human movement patterns, achieving 36 citations since 2023. He further advanced the field with interaction-based deep reinforcement learning for dense crowd flow, demonstrating robust collision avoidance in limited spaces. In underwater robotics, Zhai introduced WaterFormer, a global-local transformer with an environment adaptor for image enhancement, garnering 21 citations in 2024 by overcoming the locality limitations of CNNs. His work on efficient 3D LiDAR navigation using reinforcement learning has also been recognized for enabling autonomous operation in unknown environments. With a growing citation impact and a focus on real-world deployment, Zhai’s research is shaping the next generation of intelligent, socially-aware robots for applications ranging from service robotics to marine exploration.

Research Focus

Key Achievements

3
H-Index
4
Papers
62
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Graph Relational Reinforcement Learning for Mobile Robot Navigation in Large-Scale Crowded Environments
36 citations · 2023
📈 Most Prolific Year: 2023 (1 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: China University of Mining and Technology, Chinese University of Hong Kong

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago