Youngjae Yoo
Papers
2
Total Citations
20
H-Index
2
About
Youngjae Yoo is a robotics researcher whose work centers on enhancing the perceptual and operational reliability of autonomous systems, particularly in mobile manipulation and home service robotics. His most significant contribution lies in multimodal anomaly detection for object slip perception, where he developed a deep auto-encoder framework that fuses vision and tactile sensor data. This innovation enables mobile manipulation robots to detect and react to object slippage in real time, a critical capability for performing dexterous tasks in noisy, dynamic environments. His 2021 paper on this topic has garnered 18 citations, reflecting its impact on improving robot grasping robustness. Yoo also played a key role in Team Tidyboy’s entry at the World Robot Summit 2020, where he helped architect a modular software framework for home service robots. This system integrates verbal and non-verbal human-robot interaction with 3D perception of known objects, demonstrating a practical, scalable approach to complex domestic tasks. By bridging the gap between theoretical anomaly detection and real-world deployment, Yoo’s work advances the frontier of reliable, human-aware robotics.
Research Focus
Key Achievements
Top Papers
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- 2