Minwoo Na
Papers
8
Total Citations
98
H-Index
5
About
Minwoo Na is a robotics researcher whose work spans robotic assembly, force control, reinforcement learning, and automated inspection systems. His most impactful contributions lie at the intersection of intelligent manipulation and adaptive control, where he has pioneered methods for enabling robots to perform complex assembly tasks with greater autonomy and precision. His 2023 paper on reinforcement learning-based robotic assembly using force and visual information has garnered 46 citations, reflecting significant community interest in his hybrid sensing approach. Complementing this, his work on admittance control with adaptive stiffness (24 citations) addresses the practical challenge of assembling low-stiffness components — a notoriously difficult problem in industrial robotics. Na has also made meaningful contributions to sim-to-real transfer, developing reinforcement learning frameworks for automatically tuning impedance parameters, reducing the burden on human engineers. His inspection-related research covers CAD-based view planning and path optimization for complex-shaped objects, improving efficiency in automated quality control pipelines. More recently, he has extended his scope to robot calibration using structured-light cameras, offering cost-effective alternatives to expensive laser-tracker systems. Across his body of work, Na demonstrates a consistent drive to make robotic systems more intelligent, adaptable, and deployment-ready in real-world manufacturing environments.
Research Focus
Key Achievements
Top Papers
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- 7Calibration of robot DH parameters based on structured light camera2 citations · 2024
- 8Robotic Path Planning for Inspection of Complex-Shaped Objects2 citations · 2020