Lingli Yu
Papers
2
Total Citations
46
H-Index
2
About
Lingli Yu is a leading researcher in intelligent robotics and autonomous navigation, with a focus on advancing multi-robot coordination and deep reinforcement learning. Her most cited work, "Hybrid attention-oriented experience replay for deep reinforcement learning and its application to a multi-robot cooperative hunting problem" (2022, 43 citations), introduces a novel attention mechanism that significantly improves sample efficiency and decision-making in complex multi-agent environments. This contribution addresses critical challenges in cooperative robotics, enabling more effective and scalable team behaviors. Yu’s recent research tackles global path planning for autonomous robots by integrating artificial potential fields with soft actor-critic algorithms, overcoming traditional limitations in path smoothness and computational speed. Her work bridges theoretical advances in reinforcement learning with practical robotic applications, offering robust solutions for real-world navigation. With growing recognition in the field, Yu’s research continues to shape the future of intelligent autonomous systems, making her a notable figure for students and researchers interested in the intersection of AI, robotics, and multi-agent systems.
Research Focus
Key Achievements
Top Papers
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