Papers
16
Total Citations
329
H-Index
8
About
Seongyong Koo is a roboticist whose work spans manipulation, human-robot interaction, and cognitive systems. His most impactful research addresses the challenge of robotic picking and packing in cluttered environments—a core problem for logistics automation. As a key contributor to Team NimbRo Picking, Koo helped develop the system that competed in the 2017 Amazon Robotics Challenge, demonstrating fast object learning and dual-arm coordination for stowing, picking, and packing tasks (82 citations). In human-robot interaction, he pioneered a telepresence robot system for English tutoring (82 citations) and developed an online touch behavior recognition algorithm using a temporal decision tree classifier, enabling robots to respond naturally to physical contact. Koo also advanced intention recognition by modeling human actions like approach and depart from relative movements between human and robot. His work on transferring grasping skills to novel objects via latent space non-rigid registration addresses a fundamental challenge in open-world robotics. With over 300 total citations, Koo’s contributions bridge perception, manipulation, and social robotics, making him a notable figure in applied robotic systems for real-world environments.
Research Focus
Key Achievements
Top Papers
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- 2Telepresence robot system for English tutoring82 citations · 2010
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- 10Designing reactive emotion generation model for interactive robots6 citations · 2010