Yating Xie
Papers
2
Total Citations
25
H-Index
1
About
Yating Xie is a rising researcher at the forefront of intelligent tactile sensing, with a focus on triboelectric and capacitive sensor technologies for robotics and human-machine interaction. Her work bridges materials science and deep learning, creating hybrid sensors that mimic biological touch. In her highly cited 2024 study, Xie introduced a deep learning-assisted object recognition system using a hybrid triboelectric-capacitive tactile sensor based on porous PDMS, achieving 24 citations for its novel integration of dual sensing mechanisms. This work demonstrates how combining triboelectric sensitivity with capacitive precision can enhance robotic perception. More recently, her 2025 paper on a fur-inspired triboelectric tactile sensing array advances intelligent human-machine interfaces, drawing inspiration from nature to improve sensor adaptability. Though early in her career, Xie’s contributions are already shaping the next generation of tactile systems, with her 2024 paper gaining notable traction. Her innovative approach to sensor design and AI integration positions her as a key voice in the development of more intuitive, responsive robotic and interactive technologies.
Research Focus
Key Achievements
Top Papers
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- 2