Diversified manufacturing requires a robot system to be highly generalizable to different novel objects. Previous studies try to achieve this goal by using learning-based methods but are high-cost and lack of generalization due to insufficient knowledge in training. In this paper, we propose a new approach of novel object grasping using an object ontology to implement similarity matching between known objects and novel objects. We realize successful grasps on a novel object by imitating robust grasps from its similar known object. Our method is training free and verified for generalizability with an average success rate of 83% in novel object grasping experiments.
