Xingjie Liu
Papers
2
Total Citations
25
H-Index
2
About
Xingjie Liu is a leading researcher in intelligent robotics and autonomous systems, with a primary focus on multi-agent coordination and industrial automation. Their most impactful work, "Option-Based Multi-Agent Reinforcement Learning for Painting With Multiple Large-Sized Robots" (2022, 18 citations), introduces a novel OMARL framework that models cooperative multi-station, multi-robot systems as fully-connected graphs. This approach ensures efficient, conflict-free task allocation for complex applications like aircraft painting, addressing critical challenges in real-world manufacturing. Liu further advances industrial robotics with "High-Precision Vision Localization System for Autonomous Guided Vehicles in Dusty Industrial Environments" (2022, 7 citations), which enhances AGV navigation under harsh conditions, supporting the Fourth Industrial Revolution. By integrating reinforcement learning with robust perception, Liu’s work bridges the gap between theoretical algorithms and practical deployment, enabling safer and more efficient automation in ports, warehouses, and factories. Their contributions are pivotal for students and researchers exploring scalable multi-robot systems, offering foundational methods for conflict resolution and localization in dynamic, dusty environments.
Research Focus
Key Achievements
Top Papers
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- 2