Tingjun Li

University of Michigan–Ann Arbor, Tianjin University of Technology

Papers

Total Citations

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About

Tingjun Li is a researcher at the forefront of robotics, whose work bridges the critical gap between geometric theory and practical machine learning. His primary research areas include robot perception, state estimation, and control, with a particular focus on leveraging the inherent symmetries of physical systems. Li’s major contribution lies in developing methods that respect these geometric structures, leading to more robust and accurate sensor registration and state estimators. His most-cited work, the 2022 article "Progress in symmetry preserving robot perception and control through geometry and learning," has garnered 13 citations, establishing a foundation for a new generation of theoretically grounded robotic systems. Beyond perception, Li explores the application of intelligent robotics in construction, as seen in his 2023 paper on path optimization for prefabricated buildings. By combining hybrid algorithms with the potential of 5G technology, he is addressing key challenges in sustainable construction automation. Through his research, Tingjun Li is not only advancing the mathematical underpinnings of robotics but also paving the way for their practical deployment in complex, real-world environments.