Mingzuo Xie

China University of Mining and Technology, Stevens Institute of Technology

Papers

Total Citations

H-Index

About

Mingzuo Xie is a robotics researcher whose work spans robot kinematics education, motion planning, and the development of accessible robotic systems. With a strong focus on bridging theoretical robotics with practical application, Xie has made notable contributions to both pedagogical methods and algorithmic innovation in the field. Xie's most influential work centers on transforming how robotics is taught at the university level. His 2019 paper on teaching robot kinematics using MATLAB and V-REP (24 citations) introduced an integrated simulation-based approach that has helped instructors make complex mathematical principles more accessible to students. This work was complemented by an earlier 2018 study (5 citations) proposing a virtual robotics laboratory framework that reduces reliance on costly physical equipment while enhancing hands-on learning. On the research frontier, Xie has explored residual reinforcement learning for robotic arm motion planning (17 citations), addressing key challenges such as low training efficiency and convergence difficulties that plague traditional reinforcement learning approaches. His 2021 work on Mirobot, a low-cost 6-DOF desktop robot, further reflects his commitment to democratizing robotics education through affordable hardware solutions. Together, Xie's contributions position him as an emerging voice at the intersection of educational technology and applied robotics research.