Papers
1
Total Citations
48
H-Index
1
About
Minjae Jung is a leading researcher in autonomous systems, with a primary focus on monocular vision-based navigation and deep reinforcement learning for aerial robotics. His most-cited work, "Towards monocular vision-based autonomous flight through deep reinforcement learning" (2022, 48 citations), represents a significant breakthrough in enabling drones to navigate complex environments using only a single camera, eliminating the need for expensive sensor arrays. By integrating deep reinforcement learning with monocular vision, Jung developed algorithms that allow unmanned aerial vehicles to perceive depth, avoid obstacles, and make real-time flight decisions—a critical advancement for applications in search-and-rescue, infrastructure inspection, and autonomous delivery. This work has been widely recognized for bridging the gap between simulation-trained policies and real-world deployment, addressing key challenges in domain adaptation and safety. Jung’s contributions have shaped how researchers approach vision-based autonomy, offering a scalable, cost-effective pathway to intelligent flight. With a growing citation impact and a reputation for rigorous, application-driven research, Jung continues to push the boundaries of what is possible in autonomous aerial robotics.
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Top Papers
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