Yunfei Xiang
Papers
2
Total Citations
13
H-Index
2
About
Yunfei Xiang is a robotics researcher whose work focuses at the intersection of multi-robot coordination, autonomous exploration, and efficient on-device perception. His primary research areas include multi-agent reinforcement learning (MARL) for cooperative robotics and hardware-constrained deep learning for embedded systems. Xiang’s most significant contribution is his pioneering approach to asynchronous MARL for multi-robot cooperative exploration, which enables real-time decision-making without requiring synchronized agent updates—a critical advancement for real-world deployment. This work has garnered 11 citations since its 2023 publication, reflecting its growing influence in the field. More recently, Xiang has addressed the challenge of deploying stereo depth estimation on resource-limited autonomous aerial vehicles (AAVs). His 2025 paper introduces a hardware-friendly online adaptation method that allows lightweight neural networks to maintain accuracy through self-supervised learning, achieving real-time performance on embedded platforms. This work, already accumulating 2 citations, demonstrates Xiang’s commitment to bridging the gap between state-of-the-art algorithms and practical, deployable robotic systems. His research is particularly valuable for students and engineers working on field robotics, where computational constraints and the need for real-time adaptation are paramount.
Research Focus
Key Achievements
Top Papers
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- 2