Grasp planning by human experience on a variety of objects with complex geometry

We present an effective method of identifying the graspable components of a variety of complex objects for grasp planning based on human experience. Instead of focusing on individual objects, our method identifies graspable components on the category level under the assumption that geometrically alike objects share similar graspable components. Employing a modified SHOT descriptor, we propose a fast KNN-based method for object categorization. Then the graspable components are identified by adopting a learning framework based on human experience. Finally, a fast grasp planning method comprised of contact points exaction and hand kinematics calculation accomplishes the grasp on the identified graspable component. Our experiments demonstrate the effectiveness of this method by realizing grasps on the graspable components of human choice for a variety of unseen objects.