Weiyi Li

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About

Weiyi Li is a leading researcher in robot kinematics, machine learning for robotics, and geometric modelling. Their most influential work, "Augmented Neural Network for Full Robot Kinematic Modelling in SE(3)" (2022, 19 citations), addresses the critical challenge of modelling increasingly complex robotic structures where traditional analytical approaches fall short. Li pioneered a novel neural network framework that learns full kinematic models directly in the SE(3) Lie group, enabling robots to capture complex spatial transformations without hand-crafted equations. This contribution is particularly significant for advanced robotic systems—such as soft robots, exoskeletons, and multi-arm manipulators—where conventional modelling is impractical. By integrating geometric deep learning with robotics, Li’s work bridges a key gap between data-driven methods and rigorous mathematical foundations. Their research has been cited by teams working on robot control, motion planning, and autonomous systems, demonstrating its broad impact. Li’s approach not only simplifies model learning but also improves accuracy and generalizability, making it a cornerstone for next-generation robotic applications.