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About

Xingjian Liu is a leading researcher in robotics and automation, with key contributions spanning multirobot calibration, 3D scanning, and deformable object manipulation. His most impactful work, "Simultaneous Calibration of Multicoordinates for a Dual-Robot System by Solving the AXB = YCZ Problem" (2021, 118 citations), addresses a fundamental challenge in cooperative robotics—determining unknown transformation relationships between hand-eye and base-base coordinates, enabling precise multirobot collaboration. Liu also advanced 3D measurement with his "Fast Eye-in-Hand 3-D Scanner-Robot Calibration for Low Stitching Errors" (2020, 41 citations), which reduces data stitching errors during long-term continuous scanning, a critical improvement for industrial inspection. His innovative work extends to bio-inspired automation, as seen in "Automated Orientation Control of Motile Deformable Cells" (2022, 18 citations), where he tackles the complex control of both deformation and motion in living organisms like sperm and worms. More recently, Liu has explored deep reinforcement learning for hyper-redundant robot inverse kinematics (2022) and nonlinear MPC for wheeled humanoid robots (2025), demonstrating a commitment to solving high-dimensional, nonlinear control problems. With over 200 total citations and a growing portfolio in point cloud clustering for defect diagnosis, Liu’s research is shaping the future of intelligent robotic systems.