Seung‐Woo Seo
Papers
8
Total Citations
49
H-Index
4
About
Seung-Woo Seo is pioneering the next generation of autonomous navigation for unstructured and extreme environments. His research centers on self-supervised learning, traversability estimation, and robust reinforcement learning for off-road and mountainous terrain. Seo’s major contributions include developing adaptive, self-supervised algorithms that enable robots to estimate traversability in real-time, learning from geometric and visual cues without human labels. His work on "Adaptive Robot Traversability Estimation" (13 citations) and "Traversability-Aware Adaptive Optimization" (10 citations) directly addresses the challenge of navigating mobility-stressing elements in rugged landscapes. He has also advanced point cloud upsampling for autonomous vehicles and introduced novel frameworks like UNICON and SeRO, which ensure robust behavior in unfamiliar or out-of-distribution scenarios. Notably, his digital twin-driven anomaly detection pipeline for semiconductor bonding processes demonstrates the breadth of his impact, tackling problems with failure rates below one in ten million. With a growing citation record and a focus on real-world deployment, Seo is shaping how robots perceive and act in the world’s most challenging terrains.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8