Rigid Grasp Candidate Generation for Assembly Tasks

In this study, a new method is proposed to generate rigid grasp candidates for assembly tasks. Although the majority of the recent research is aimed at the generation of candidates for grasping several types of objects, our method focuses on tasks requiring precise and rigid contact. For instance, assembling furniture requires higher grasping force and precision than bin picking tasks or pick and place tasks. This means that the pose information of the object should be accurate for assembly tasks, and the object should not slip while being held. Given these constraints, we generated possible grasp pose candidates. The proposed algorithm is verified by performing real robot experiments in which furniture parts are assembled.