Xuebo Ji
Papers
1
Total Citations
16
H-Index
1
About
Xuebo Ji is a leading researcher in multi-agent systems and robot navigation, whose work addresses the fundamental challenge of coordinating large teams of robots in complex, obstacle-filled environments. His most-cited paper, "Decentralized, Unlabeled Multi-Agent Navigation in Obstacle-Rich Environments using Graph Neural Networks" (2021, 16 citations), introduces a groundbreaking learning-based approach that allows robots to autonomously solve three intertwined problems simultaneously: assigning goals, avoiding collisions, and navigating safely. By leveraging graph neural networks, Ji’s method enables fully decentralized decision-making, where each robot infers its optimal action without requiring a central controller or pre-labeled targets. This work is particularly impactful for real-world applications like warehouse logistics, search-and-rescue, and autonomous drone swarms, where scalability and robustness are critical. Ji’s contributions have been recognized for their elegance and practicality, offering a scalable solution to a notoriously difficult problem in robotics. With a growing citation record, his research continues to influence the fields of distributed intelligence and multi-robot coordination, making him a key figure to watch for students and researchers interested in the future of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1