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

Grasping objects intelligently is a challenging task even for humans, and we spend a considerable amount of time during our childhood to learn how to grasp objects correctly. In the case of robots, we cannot afford to spend that much time on making it to learn how to grasp objects effectively. Therefore, in the present research we propose an efficient learning architecture based on VQVAE so that robots can be taught with sufficient data corresponding to correct grasping. However, getting sufficient labelled data is extremely difficult in the robot grasping domain. To help solve this problem, a semi-supervised learning-based model, which has much more generalization capability even with limited labelled data set, has been investigated. Its performance shows 6% improvement when compared with existing state-of-the-art models including our earlier model. During experimentation, it has been observed that our proposed model, RGGCNN2, performs significantly better, both in grasping isolated objects as well as objects in a cluttered environment, compared to the existing approaches 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 and exploiting the high-quality learning ability of GGCNN2 architecture with the limited number of labelled dataset together with the learned latent embeddings, can be used as a de-facto training method which has been established and also validated in this paper through rigorous hardware experimentations using Baxter (Anukul) research robot ( Video demonstration ).