Seungyeon Yoo

I am a Ph.D. candidate at Seoul National University, advised by Prof. H. Jin Kim. My research interests include vision-based navigation, visual learning, and robotics.

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Research

My research focuses on building RGB-only autonomous robot systems. I have been exploring GS-based real-to-sim data generation to address the sim-to-real gap, monocular-camera-based target chasing through cross-modal learning, and single-view RL manipulation leveraging NeRF. I apply these approaches across diverse robot platforms, including drones, manipulators, and ground robots. I believe that RGB-focused robot systems can serve as a key pathway toward realizing human-level intelligence in robotics.

We propose a novel RL framework that extracts 3D-aware representations from single-view RGB input, without requiring camera pose or synchronized multi-view images during the downstream RL.

Drift in visual pose estimation is eliminated through plane-based joint optimization and the registration module.

Cross-modal representation enables RGB-only target chasing instead of multiple sensor inputs.

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