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About

Fangli Mou is a robotics researcher whose work spans the critical intersection of space robotics, intelligent control, and autonomous manipulation. Her most impactful contribution addresses one of the most challenging problems in on-orbit servicing: the contact dynamics and control of a space robot capturing a tumbling object (74 citations). This foundational work has been extended to three-dimensional capture scenarios and validated through her development of a hardware-in-the-loop simulation facility for the Chinese Space Station Manipulator, demonstrating her commitment to bridging theory with practical ground verification. Mou has also made significant advances in terrestrial robotics, developing disturbance rejection sliding mode control methods that enhance robot tracking accuracy in unstructured environments. Her recent work incorporates deep learning for pose estimation in robotic insertion tasks (28 citations) and tackles the difficult problem of modeling rigid-flexible coupling dynamics for heavy industrial cable manipulation. Most recently, her MetaTra framework applies meta-learning to generalized trajectory prediction across unseen domains, showcasing her forward-looking approach to creating more adaptable robotic systems. With over 140 total citations, Mou's research portfolio demonstrates a rare ability to address both the extreme precision demands of space operations and the complex uncertainties of real-world robotic manipulation.