Hyunyoung Jung
Papers
2
Total Citations
25
H-Index
2
About
Hyunyoung Jung is a leading researcher in legged robotics, specializing in the intersection of model-based control and reinforcement learning for dynamic locomotion and manipulation. Their work addresses the fundamental challenge of enabling robots—from quadrupeds to humanoids—to perform robust, agile, and versatile tasks in complex environments. Jung’s major contribution is the development of frameworks that seamlessly integrate the precision of optimal control with the adaptability of learning. Their highly cited work, “Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion” (2023, 17 citations), introduces the IFM framework, which combines model predictive control with imitation learning to achieve robust, symmetric gaits. Building on this, their 2025 paper “Opt2Skill” (8 citations) pioneers a method for humanoid robots to imitate dynamically-feasible whole-body trajectories, enabling sophisticated loco-manipulation tasks. By bridging the gap between theoretical optimal control and practical learning algorithms, Jung’s research is shaping the next generation of autonomous, physically-capable robots, with direct implications for search-and-rescue, industrial automation, and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2