Zhihui Shen

Beijing University of Technology, Nantong University

Papers

Total Citations

H-Index

About

Zhihui Shen is a rising researcher at the forefront of robotic perception and intelligent tactile sensing. Her work bridges the gap between computer vision and physical interaction, with a primary focus on enhancing robotic grasp detection and shape recognition. In her highly cited 2024 paper, "HBGNet: Robotic Grasp Detection Using a Hybrid Network" (4 citations), Shen addresses a critical limitation in existing grasp-detection methods: the inadequate consideration of multiscale object information. By proposing a novel hybrid network with skip connections, she significantly improves grasp accuracy in complex, cluttered environments—a vital step for real-world robotic manipulation. Complementing this, her research on "FBG Tactile Sensing Shape Recognition Based on Convolutional Neural Network" (also 4 citations) tackles the challenge of low-efficiency shape identification in flexible robotic skin. By integrating Fiber Bragg Grating (FBG) sensors with a convolutional neural network, Shen has developed a method that dramatically expands the range of recognizable shapes and boosts recognition speed. Though early in her career, Shen’s dual contributions to both visual and tactile perception demonstrate a holistic approach to robotics, positioning her as a key innovator in creating more adaptive and perceptive robotic systems for complex, unstructured environments.