Jun-Young Baek
Papers
5
Total Citations
52
H-Index
4
About
Jun-Young Baek’s research career is defined by a singular focus: making robots safer, smarter, and more autonomous in human environments. His foundational work in collision detection and compliant control for service robot arms—most notably the “Current based compliance control method for minimizing an impact force at collision of service robot arm” (21 citations)—established critical safety frameworks for robots operating alongside people. Baek pioneered real-time collision detection without torque sensors, enabling cost-effective, robust safety systems for mobile manipulators. Expanding into perception and autonomy, he developed a position tracking system using a single RGB-D camera for UAV control evaluation in GPS-denied environments (9 citations), offering a low-cost alternative to expensive motion capture systems. His work on detecting elevator doors with stereo vision (9 citations) directly addressed the challenge of multi-floor navigation for service robots. Most recently, Baek has advanced into human-robot interaction with a 2025 study on machine learning-driven muscle fatigue estimation during resistance training with assistive robotics, bridging rehabilitation and performance optimization. With over 50 total citations across these key contributions, Baek’s career demonstrates a consistent drive to solve practical, real-world robotics problems—from collision safety to autonomous navigation and human augmentation.
Research Focus
Key Achievements
Top Papers
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