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Grasping objects intelligently is a challenging task even for humans and wespend a considerable amount of time during our childhood to learn how to graspobjects correctly. In the case of robots, we can not afford to spend that muchtime on making it to learn how to grasp objects effectively. Therefore, in thepresent research we propose an efficient learning architecture based on VQVAEso that robots can be taught with sufficient data corresponding to correctgrasping. However, getting sufficient labelled data is extremely difficult inthe robot grasping domain. To help solve this problem, a semi-supervisedlearning based model which has much more generalization capability even withlimited labelled data set, has been investigated. Its performance shows 6\%improvement when compared with existing state-of-the-art models including ourearlier model. During experimentation, It has been observed that our proposedmodel, RGGCNN2, performs significantly better, both in grasping isolatedobjects as well as objects in a cluttered environment, compared to the existingapproaches which do not use unlabelled data for generating grasping rectangles.To the best of our knowledge, developing an intelligent robot grasping model(based on semi-supervised learning) trained through representation learning andexploiting the high-quality learning ability of GGCNN2 architecture with thelimited number of labelled dataset together with the learned latent embeddings,can be used as a de-facto training method which has been established and alsovalidated in this paper through rigorous hardware experimentations using Baxter(Anukul) research robot.

Development of a robust cascaded architecture for intelligent robot grasping using limited labelled data