Joohwan Seo
Papers
8
Total Citations
61
H-Index
5
About
Joohwan Seo is a rising star at the intersection of geometric deep learning and robotic manipulation. His research centers on leveraging the mathematical structure of the Lie group SE(3) to create more intelligent, transferable, and physically-aware robot control systems. Seo’s major contributions lie in developing **SE(3)-equivariant** frameworks—both for learning and control—that allow robots to generalize manipulation skills across different poses and environments without retraining. His seminal work, *Diffusion-EDFs* (2024, 18 citations), introduces a bi-equivariant denoising generative model that learns stochastic human demonstrations for visual robotic manipulation, achieving state-of-the-art performance in contact-rich tasks. He has also pioneered **Geometric Impedance Control on SE(3)** (2023, 14 citations), providing a differential geometric foundation for compliant interaction with unknown environments. With over 60 cumulative citations and a comprehensive tutorial survey on SE(3)-equivariant robot learning (2025), Seo is establishing a unified geometric language for robot learning and control. His work promises to make robots not only more dexterous but also more principled in their understanding of physical space.
Research Focus
Key Achievements
Top Papers
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- 2Geometric Impedance Control on SE(3) for Robotic Manipulators14 citations · 2023
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- 4SE(3)-equivariant Robot Learning and Control: A Tutorial Survey6 citations · 2025
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