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Moonkyu Jung is a leading researcher in legged robotics, specializing in reinforcement learning, whole-body manipulation, and high-speed navigation for quadrupedal systems. His work bridges the gap between theoretical control algorithms and real-world robotic performance, with a focus on enabling robots to operate efficiently in complex and unstructured environments. Jung’s most-cited paper, “Not Only Rewards but Also Constraints” (2024, 58 citations), introduces a novel framework that integrates constraints into neural network-based controllers, significantly improving locomotion stability and task performance. His 2023 study on “Learning Whole-Body Manipulation for Quadrupedal Robot” (40 citations) pioneers a hierarchical control strategy that allows robots to manipulate large objects using their entire body, a breakthrough for industrial and disaster-response applications. More recently, Jung has advanced high-speed navigation on discrete terrain (2025, 5 citations) and developed RAIBO2 (2025, 3 citations), a highly efficient quadruped that completed a full marathon on a single battery charge—demonstrating unprecedented energy efficiency. His work consistently pushes the boundaries of autonomous legged locomotion, earning recognition for its practical impact and innovative integration of learning-based methods with physical constraints.