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About

Xiaowei Shi is a pioneering researcher in bio-inspired tactile sensing and robotic perception, with a focus on developing intelligent systems that mimic human touch. Their key contributions include designing an improved bioinspired tactile fingertip for surface recognition (2023, 17 citations), which enables robots to perceive object textures and forces with unprecedented accuracy. Shi’s work on contact-dominated localized electric-displacement-field-enhanced pressure sensing (2025, 14 citations) addresses critical limitations in capacitive sensors, offering enhanced performance for flexible electronics and humanoid robots. They have also advanced deep learning applications in tactile perception, as seen in their 2024 study on automatic feature transfer for surface recognition (8 citations), reducing reliance on hand-crafted features. Beyond tactile sensing, Shi has innovated in agricultural technology with an improved YOLO algorithm for weed detection in soybean fields (2025, 13 citations) and in medical imaging with a long-term reprojection loss method for self-supervised depth estimation in endoscopic surgery (2024, 4 citations). Their interdisciplinary approach—spanning robotics, materials science, and computer vision—has garnered over 67 citations, establishing Shi as a rising leader in creating smarter, more perceptive machines for real-world applications.