Linling Xu
Papers
1
Total Citations
3
H-Index
1
About
Linling Xu is a researcher whose work sits at the intersection of robotics, artificial intelligence, and autonomous navigation. Her primary research focuses on developing intelligent motion planning algorithms that enable mobile robots to navigate safely and efficiently through dynamic, crowded environments. Xu’s major contribution lies in the integration of criticality theory with deep reinforcement learning (DRL), a novel approach that addresses the persistent challenge of real-time collision avoidance. Her most cited paper, "Criticality-Guided Deep Reinforcement Learning for Motion Planning" (2021), proposes a framework that intelligently prioritizes high-risk scenarios during training, allowing robots to make faster, safer decisions without sacrificing computational efficiency. This work has garnered 3 citations and is recognized for pushing the boundaries of DRL-based navigation. By bridging the gap between theoretical safety metrics and practical deployment, Xu’s research has significant implications for autonomous vehicles, service robots, and industrial automation. Her achievements highlight a commitment to creating robust, adaptive systems that can operate reliably in unpredictable real-world settings, making her a promising voice in the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1Criticality-Guided Deep Reinforcement Learning for Motion Planning3 citations · 2021