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Precision is a crucial performance indicator for robot arms, as highprecision manipulation allows for a wider range of applications. Traditionalmethods for improving robot arm precision rely on error compensation. However,these methods are often not robust and lack adaptability. Learning-basedmethods offer greater flexibility and adaptability, while current researchesshow that they often fall short in achieving high precision and struggle tohandle many scenarios requiring high precision. In this paper, we propose anovel high-precision robot arm manipulation framework based on online iterativelearning and forward simulation, which can achieve positioning error(precision) less than end-effector physical minimum displacement. Additionally,we parallelize multiple high-precision manipulation strategies to bettercombine online iterative learning and forward simulation. Furthermore, weconsider the joint angular resolution of the real robot arm, which is usuallyneglected in related works. A series of experiments on both simulation and realUR3 robot arm platforms demonstrate that our proposed method is effective andpromising. The related code will be available soon.

High-Precise Robot Arm Manipulation based on Online Iterative Learning  and Forward Simulation with Positioning Error Below End-Effector Physical  Minimum Displacement