Skill learning in robot polishing is gaining attention and becoming a hot issue. Current studies on skill learning in robot polishing are mainly about trajectory skills, and force-relevant skills learning models are less studied. A skill learning method with good generalization and robustness is one of the elements worth investigating. In this study, a force-relevant skills learning method called arc-length probabilistic movement primitives (AL-ProMP) is proposed to improve the efficiency of robot polishing force planning. AL-ProMP learns the mapping between the contact force and polishing trajectory, and the temporal scaling factor and force scaling factor in AL-ProMP enable better robustness of force planning in speed scaling tasks and polishing tasks in different scenarios. Speed scaling is an important property for adaptation of the polishing policy. For the generalization of polishing skills to different polishing tools in robotics disc polishing tasks of unknown geometric model workpieces, a novel force scaling factor for different polishing discs is proposed according to the contact force model. In addition, polishing contact position learning provides the basis for polishing trajectory generalization. Finally, it is experimentally verified that the proposed method is effective in learning and generalizing the demonstrated skills and improving the polishing surface quality of the workpiece with unknown geometric model.

机器人打磨的技能学习越来越受到关注，成为热点问题。目前对机器人抛光技能学习的研究主要是轨迹技能，与力相关的技能学习模型研究较少。具有良好泛化性和鲁棒性的技能学习方法是值得研究的要素之一。在这项研究中，提出了一种称为弧长概率运动原语（AL-ProMP）的力相关技能学习方法，以提高机器人抛光力规划的效率。AL-ProMP 学习接触力和抛光轨迹之间的映射，并且 AL-ProMP 中的时间比例因子和力比例因子能够在不同场景下的速度缩放任务和抛光任务中实现更好的力规划鲁棒性。速度缩放是调整抛光策略的一个重要属性。为了将抛光技能推广到未知几何模型工件的机器人圆盘抛光任务中的不同抛光工具，根据接触力模型提出了一种新的不同抛光盘的力比例因子。此外，抛光接触位置学习为抛光轨迹泛化提供了基础。最后通过实验验证了所提出的方法在学习和推广所展示的技能以及提高未知几何模型工件的抛光表面质量方面的有效性。根据接触力模型，提出了一种适用于不同抛光盘的新型力比例因子。此外，抛光接触位置学习为抛光轨迹泛化提供了基础。最后通过实验验证了所提出的方法在学习和推广所展示的技能以及提高未知几何模型工件的抛光表面质量方面的有效性。根据接触力模型，提出了一种适用于不同抛光盘的新型力比例因子。此外，抛光接触位置学习为抛光轨迹泛化提供了基础。最后通过实验验证了所提出的方法在学习和推广所展示的技能以及提高未知几何模型工件的抛光表面质量方面的有效性。