Taerim Yoon
Papers
4
Total Citations
29
H-Index
3
About
Taerim Yoon is a robotics researcher whose work bridges perception, control, and human-robot interaction. His research focuses on three key areas: active visual search, soft robot modeling, and teleoperation. In his highly cited work on Zero-shot Active Visual Search (ZAVIS), Yoon introduced a novel approach enabling mobile robots to locate objects described in free-form text without predefined categories—a significant leap for assistive robotics. He also pioneered Kinematics-Informed Neural Networks (KINN), a method that dramatically improves generalization in soft robot model identification by embedding physical constraints directly into the learning architecture, addressing the critical challenge of data scarcity in soft robotics. Yoon’s contributions to quality-diversity based semi-autonomous teleoperation further advance robot controllability, allowing users to guide robots through diverse behaviors rather than repetitive solutions. With his most cited paper accumulating 12 citations since 2023, Yoon’s work is already shaping how robots search, move, and collaborate with humans. His recent 2025 work on spatio-temporal motion retargeting for quadruped robots demonstrates his continued push toward more dynamic and adaptable robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Spatio-Temporal Motion Retargeting for Quadruped Robots2 citations · 2025