Publications

Contraction Estimator: a Global Optimal Solution for Robotics Optimization Problems

Published in Benelux Meeting on Systems and Control, 2026

In this work, we propose a global optimimal framework for optimization problems (convex, non-convex, non-differentiable and discrete) on complete Banach spaces.

Recommended citation: Xinghua, Liu and Ming, Cao. "Contraction Estimator: a Global Optimal Solution for Robotics Optimization Problems." The 45th Benelux Meeting on Systems and Control, 2026, p. 117. https://beneluxmeeting.be/2026/uploads/boa2026.pdf

Robust simultaneous UWB-anchor calibration and robot localization for emergency situations

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2025

This work proposed an FGO framework for simultaneously calibration and localization in robotics.

Recommended citation: X. Liu and M. Cao, "Robust simultaneous UWB-anchor calibration and robot localization for emergency situations," 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, 2025, pp. 4691-4697, doi: 10.1109/SMC58881.2025.11342538. https://ieeexplore.ieee.org/abstract/document/11342538

High-order regularization dealing with ill-conditioned robot localization problems

Published in IEEE Transactions on Robotics, 2025

This work is about the high-order regularization to solve ill-conditioned inverse problems in robotics.

Recommended citation: X. Liu and M. Cao, "High-Order Regularization Dealing With ILL-Conditioned Robot Localization Problems," in IEEE Transactions on Robotics, vol. 41, pp. 3539-3555, 2025, doi: 10.1109/TRO.2025.3562487. https://ieeexplore.ieee.org/abstract/document/10969984

High-order regularization in machine learning and learning-based control

Published in Benelux Meeting on Systems and Control, 2024

This work is about the high-order regularization in machine learning and learning-based control.

Recommended citation: Xinghua, Liu and Ming, Cao. "High-order regularization in machine learning and learning-based control." The 43rd Benelux Meeting on Systems and Control, 2024, p. 80. https://LiuxhRobotAI.github.io/files/High-order-regularization-in-machine-learning-and-learning-based-control.pdf

A high-order regularization method and approximate solution

Published in Benelux Meeting on Systems and Control, 2023

This work is about the high-order regularization method and its application in ill-conditioned inverse problems in robot localization.

Recommended citation: Xinghua, Liu and Ming, Cao. "A high-order regularization method and approximate solution." The 42nd Benelux Meeting on Systems and Control , 2023, p. 198. https://LiuxhRobotAI.github.io/files/A-high-order-regularization-method-and-approximate-solution.pdf

See Google Scholar for more details about my work.