Yueyuan Sui
Papers
1
Total Citations
14
H-Index
1
About
Yueyuan Sui is a researcher advancing the frontiers of resilient multi-robot systems through the integration of graph neural networks and multi-agent reinforcement learning. Their work addresses a critical gap in real-world robotics: the fragility of coordination algorithms under realistic disturbances like agent failures and communication disruptions. In their highly cited 2024 paper, Sui pioneered a graph neural network-based multi-agent reinforcement learning framework that enables distributed coordination to withstand such anomalies, achieving 14 citations in a short period—a strong indicator of the work's timely impact. This contribution is particularly significant for field robotics, where system resilience is paramount. Sui's research elegantly bridges graph theory and reinforcement learning, offering a scalable solution that maintains performance even when robots are lost or links are broken. Their work not only advances theoretical understanding but also provides practical pathways for deploying robust multi-robot teams in challenging environments, from search-and-rescue to environmental monitoring.
Research Focus
Key Achievements
Top Papers
- 1