Yingrui Jin

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About

Yingrui Jin’s research lies at the intersection of robotics, computer vision, and artificial intelligence, with a focus on enabling autonomous systems to perceive and interact with their environments. Jin’s most impactful work centers on the NAO humanoid robot, where they have pioneered methods for target localization, grasping, and navigation path optimization. A standout contribution is the integration of the YOLOv8 deep learning network with monocular ranging techniques, a combination that dramatically improves both the speed and accuracy of object recognition and distance estimation—overcoming a key limitation of traditional monocular systems, which suffer from increasing error at longer ranges. This work, published in 2023, has already garnered 19 citations, signaling its rapid influence in the field. Jin’s earlier 2022 study on monocular vision-based positioning models for NAO robots laid the groundwork for these advances, earning additional citations. By fusing state-of-the-art neural networks with classical geometric approaches, Jin is helping to make robotic grasping and navigation more reliable and practical, with clear applications in service robotics, automation, and assistive technology.