Geonhyup Lee

I am a Ph.D. Student in AI Convergence at Gwangju Institute of Science and Technology (GIST), advised by Prof. Kyoobin Lee. I develop scalable frameworks for multimodal action learning, focusing on collecting large-scale datasets—including vision, force, and tactile inputs—to train models that generalize to contact-rich tasks. Ultimately, I aim to enable human-level bimanual manipulation.

Prior to my doctoral studies, I earned a Master's degree in Integrated Technology from GIST, supervised by Prof. Jungwon Yoon. My research focused on the specialized area of optimal design and control of cable-driven parallel robots (CDPR) integrated with series elastic actuators, where I developed frameworks to enhance system versatility and wrench feasibility.

Geonhyup Lee

News

Selected Publications

ManipForce
ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation
Geonhyup Lee, Youngjin Lee, Kangmin Kim, Seongju Lee, Sangjun Noh, Seunghyeok Back, and Kyoobin Lee
IEEE International Conference on Robotics and Automation (ICRA) 2026
BiGraspFormer
BiGraspFormer: End-to-End Bimanual Grasp Transformer
Kangmin Kim, Seunghyeok Back, Geonhyup Lee, Sangbeom Lee, Sangjun Noh, and Kyoobin Lee
IEEE International Conference on Robotics and Automation (ICRA) 2026
AssemFormer
AssemFormer: Generalizable Vision-Force Guided Multi-Pin Assembly via Zero-Shot Sim-to-Real Approach
Geonhyup Lee, Sungho Shin, Joosoon Lee, Kangmin Kim, Sangbeom Lee, Youngjin Lee, and Kyoobin Lee
Under Review
3D Flow Diffusion Policy
3D Flow Diffusion Policy: Visuomotor Policy Learning via Generating Flow in 3D Space
Sangjun Noh, Dongwoo Nam, Kangmin Kim, Geonhyup Lee, Yeonguk Yu, Raeyoung Kang, and Kyoobin Lee
Under Review
GraspClutter6D
GraspClutter6D: A Large-scale Dataset for Robotic Grasping and Perception in Clutter
Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee, Raeyong Kang, Sangbeom Lee, Sangjun Noh, and Kyoobin Lee
IEEE Robotics and Automation Letters (RA-L) 2025
PolyFit
PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
Geonhyup Lee*, Joosoon Lee*, Sangjun Noh, Minhwan Ko, Kangmin Kim, and Kyoobin Lee
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024
T-NSRE
Comparative study on overground gait of stroke survivors with a conventional cane and a haptic cane
Hosu Lee, Amre Eizad, Geonhyup Lee, Muhammad Raheel Afzal, Jiyong Yoon, Min-Kyun Oh, and Jungwon Yoon
IEEE Transactions on Neural Systems and Rehabilitation Engineering (T-NSRE) 2021

Awards & Honors