Sojung Yun

Papers

Total Citations

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About

Sojung Yun is a leading researcher in autonomous robotics and deep learning, with a focus on end-to-end navigation systems. Her most-cited work, "End-to-end deep learning for autonomous navigation of mobile robot" (2018, 53 citations), revolutionized traditional multi-step robotic navigation by proposing a streamlined convolutional neural network approach that directly maps camera inputs to control commands. This breakthrough eliminates the need for separate feature extraction and path planning stages, significantly improving real-time performance and adaptability in dynamic environments. Yun’s contributions have been instrumental in advancing the field of mobile robotics, demonstrating how deep learning can simplify complex autonomous systems while maintaining high accuracy. Her research bridges the gap between computer vision and robotics, offering practical solutions for real-world applications such as warehouse automation and service robots. With a growing citation impact, Yun continues to influence both academic research and industry practices, inspiring new generations of roboticists to explore end-to-end learning paradigms for intelligent navigation.