Yingjie Tang
Papers
2
Total Citations
44
H-Index
2
About
Yingjie Tang is a pioneering researcher at the intersection of robotic perception and advanced tactile sensing, whose work addresses fundamental challenges in how machines interact with the physical world. Tang’s primary research areas include computer vision for transparent object manipulation and high-density flexible tactile sensor technology. In their landmark 2021 work, “DepthGrasp,” Tang introduced a novel self-attentive adversarial network with spectral residual analysis to solve the long-standing problem of depth completion for transparent objects—surfaces that confound conventional depth cameras due to reflection and refraction. This contribution, which has garnered 39 citations, enables robots to reliably grasp glassware and other transparent items, a critical capability for industrial and service robotics. More recently, in 2024, Tang achieved a breakthrough in tactile sensing by developing flexible active-matrix sensor arrays boasting an unprecedented density of 4,096 pixels per square centimeter with in-array sensitivity of 51 kPa⁻¹. This work, already cited 5 times, overcomes the traditional trade-off between pixel density and sensitivity by mitigating signal crosstalk, bringing robots closer to human-like tactile perception. Tang’s dual focus on visual and tactile modalities positions them as a key innovator in creating more dexterous, perceptive robotic systems.
Research Focus
Key Achievements
Top Papers
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