Yongyi Chen
Papers
1
Total Citations
6
H-Index
1
About
Yongyi Chen is a researcher whose work sits at the intersection of robotics, deep learning, and autonomous system security. His most prominent contribution is the development of TSAN (Temporal Self-Attention Network), a novel deep learning-based method for detecting sensor anomalies in mobile robots—a critical capability for achieving long-term autonomy. This work, published in 2024 and already garnering 6 citations, addresses the pressing challenge of injection attacks that can compromise robot sensors, threatening stable, extended operation. Chen’s research is notable for its practical focus on real-world robotic resilience, offering a robust defense mechanism against adversarial interference. By advancing anomaly detection in autonomous systems, he is helping to bridge the gap between theoretical AI and reliable, deployable robotics. His contributions are particularly relevant for researchers working on safe, long-duration robot missions in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1