Jeong-Min Choi
Papers
2
Total Citations
20
H-Index
2
About
Jeong-Min Choi is a robotics researcher whose work bridges machine learning and autonomous navigation for vision-based mobile robots. His primary research focus lies in developing self-learning algorithms that enable robots to perceive and navigate their environments without explicit programming, a critical step toward truly autonomous systems. His most influential contribution, the 2011 paper "Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms," has garnered 18 citations, establishing a foundation for integrating computer vision with adaptive control strategies. This work demonstrated how robots could learn from visual input to make real-time navigation decisions, reducing reliance on pre-mapped environments. Choi’s earlier 2010 conference paper further explored these concepts, showcasing iterative improvements in machine learning-driven robotic autonomy. While his citation count reflects a focused but impactful niche, his contributions are particularly notable for advancing the practical application of reinforcement learning in robotics. For students and researchers, Choi’s work offers a clear example of how machine learning can transform robotic perception and decision-making, making his research a valuable reference for those exploring self-learning systems in mobile robotics.
Research Focus
Key Achievements
Top Papers
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- 2