Yangqing Ye
Papers
3
Total Citations
43
H-Index
2
About
Yangqing Ye is a researcher advancing the frontier of robotic perception and dexterous manipulation, with a focus on integrating deep learning and sensor-driven control for real-world applications. His work addresses critical challenges in home service robotics, soft robotics, and skill acquisition through learning from demonstration. Ye’s most cited paper, "Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots" (2023, 26 citations), tackles the problem of motion-induced image blur in indoor environments, enabling robots to accurately identify and locate objects in real time—a vital capability for autonomous service tasks. In "Sensor Embedded Soft Fingertip for Precise Manipulation and Softness Recognition" (2021, 15 citations), he developed a novel tactile sensor that mimics human fingertip sensing, allowing robotic hands to perceive force, detect slip, and recognize object softness, significantly improving grasping precision. His latest work, "Learning Manipulation from Expert Demonstrations Based on Multiple Data Associations and Physical Constraints" (2025), explores how robots can acquire complex skills by observing human actions, moving beyond simple imitation to incorporate physical reasoning. With a growing citation impact, Ye’s contributions are shaping the next generation of intelligent, sensor-rich robotic systems for human-centric environments.
Research Focus
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Top Papers
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