Mao Shi

Tsinghua–Berkeley Shenzhen Institute, South China University of Technology

Papers

Total Citations

H-Index

About

Mao Shi is a researcher focused on advancing robotic perception and assistive technologies, with key contributions in multimodal learning for surface material perception and shared-control robotic systems. In their highly cited 2022 work, "Surface Material Perception Through Multimodal Learning," Shi tackles the ill-posed problem of disentangling material, lighting, and geometry in visual data—a critical challenge for scene understanding and robotic manipulation. This paper has garnered 7 citations, reflecting its impact on the field. Earlier, in 2018, Shi co-authored "CNN and PCA Based Visual System of A Wheelchair Manipulator Robot for Automatic Drinking," which has 6 citations. This work demonstrates a practical application of computer vision and deep learning to assist individuals with paralysis in activities of daily living, specifically enabling a wheelchair-mounted robot to autonomously detect and perform drinking tasks. By integrating convolutional neural networks with principal component analysis, Shi’s system enhances the independence and quality of life for disabled users. Together, these contributions highlight Shi’s dedication to bridging perception research with real-world assistive robotics, making their work both technically innovative and socially impactful.