  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 can not 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.
