Yi Lun Lee
Papers
1
Total Citations
3
H-Index
1
About
Yi Lun Lee is a researcher at the intersection of robotics, computer vision, and artificial intelligence, with a focus on developing autonomous systems for real-world service applications. Lee’s most cited work, "Camera-in-Hand Robotic Arm Using a Deep Neural Network to Realize Unmanned Store Service" (2019, 3 citations), introduces a novel approach to integrating deep neural networks with robotic manipulation for fully automated retail environments. This research addresses the pressing need to reduce human labor costs by enabling robotic arms to perceive, grasp, and interact with objects using an eye-in-hand camera system. Lee’s contribution lies in bridging computer vision and robotic control, demonstrating how deep learning can empower machines to perform complex tasks like ordering, making, and delivering items in an unmanned store setting. While early in citation impact, this work represents a foundational step toward scalable automation in commerce. Lee’s research is particularly relevant for students and engineers exploring practical AI-driven robotics, offering a clear pathway from neural network perception to tangible robotic action in service industries.
Research Focus
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Top Papers
- 1