AL-ProMP: Force-relevant skills learning and generalization method for robotic polishing

Abstract

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.