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About

Xinming Li is a multidisciplinary researcher whose work spans flexible electronics, advanced sensing systems, and robot perception technologies. His early research made significant strides in graphene-based sensing materials, most notably demonstrated in his 2017 paper on ultrafast dynamic pressure sensors using graphene hybrid structures, which garnered 116 citations and established him as a notable contributor to the field of electronic skin and wearable sensing. That same year, he pioneered the integration of graphene sensors with electrochromic devices, enabling real-time strain visualization inspired by nature's color-changing mechanisms. Li's research trajectory has since evolved toward intelligent robotic perception, where he has developed vision-based tactile sensing systems capable of multimodal contact information perception through neural networks — work that has quickly attracted attention within the robotics community. His 2023 contributions further advanced kinesthetic sensing through machine learning-driven decoupling of complex deformation signals, reflecting a sophisticated understanding of human-robot interaction challenges. More recently, Li has explored generative AI applications in tactile sensing, employing diffusion models to synthesize tactile images from contact conditions. Across his career, Li's cumulative impact reflects a researcher adept at bridging materials science, flexible electronics, and intelligent robotics — consistently pushing the boundaries of how machines sense and interpret the physical world.