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About

Jaesug Jung is a robotics researcher whose work spans humanoid robot control, manipulation, and compliant motion systems. He first gained international recognition through his involvement with Team SNU (Seoul National University) at the 2015 DARPA Robotics Challenge Finals, where the team demonstrated sophisticated control strategies for the THORMANG humanoid robot platform — contributions documented across multiple highly cited publications earning nearly 50 combined citations. This work established Jung as a key contributor to real-world humanoid robot deployment under demanding, unstructured conditions. His subsequent research has deepened the theoretical foundations of torque-controlled humanoid systems, particularly addressing the practical challenges of joint elasticity and time delay in position tracking — problems that significantly affect robot performance in compliant, contact-rich tasks. His operational space control frameworks for elastic-joint humanoids represent meaningful advances in bridging theory and physical implementation. More recently, Jung has expanded into robot learning and dexterous manipulation, with his 2023 paper on anthropomorphic grasping using neural shape completion reflecting a pivot toward AI-integrated robotics. His work on variable stiffness control using LSTM networks and a novel harmonic reducer design further demonstrates the breadth of his engineering contributions, spanning control theory, machine learning, and mechanical design for next-generation robotic systems.