Precision is a crucial performance indicator for robot arms, as high precision manipulation allows for a wider range of applications. Traditional methods for improving robot arm precision rely on error compensation. However, these methods are often not robust and lack adaptability. Learning-based methods offer greater flexibility and adaptability, while current researches show that they often fall short in achieving high precision and struggle to handle many scenarios requiring high precision. In this paper, we propose a novel high-precision robot arm manipulation framework based on online iterative learning 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 better combine online iterative learning and forward simulation. Furthermore, we consider the joint angular resolution of the real robot arm, which is usually neglected in related works. A series of experiments on both simulation and real UR3 robot arm platforms demonstrate that our proposed method is effective and promising. The related code will be available soon.

翻译：精度是机械臂的关键性能指标，高精度操控可扩展其应用范围。传统提升机械臂精度的方法依赖误差补偿，但这些方法通常鲁棒性不足且缺乏适应性。基于学习的方法具有更强的灵活性与适应性，然而现有研究表明，此类方法往往难以达到高精度，且难以处理诸多需要高精度的场景。本文提出一种新颖的高精度机械臂操控框架，其基于在线迭代学习与正向仿真，可实现定位误差（精度）低于末端执行器物理最小位移。此外，我们通过并行化多种高精度操控策略，更好地结合了在线迭代学习与正向仿真。同时，我们考虑了实际机械臂的关节角分辨率——这一因素在相关研究中常被忽略。在仿真与真实UR3机械臂平台上开展的一系列实验表明，所提方法有效且前景广阔。相关代码将稍后开源。