  For a robot to perform complex manipulation tasks, it is necessary for it to
have a good grasping ability. However, vision based robotic grasp detection is
hindered by the unavailability of sufficient labelled data. Furthermore, the
application of semi-supervised learning techniques to grasp detection is
under-explored. In this paper, a semi-supervised learning based grasp detection
approach has been presented, which models a discrete latent space using a
Vector Quantized Variational AutoEncoder (VQ-VAE). To the best of our
knowledge, this is the first time a Variational AutoEncoder (VAE) has been
applied in the domain of robotic grasp detection. The VAE helps the model in
generalizing beyond the Cornell Grasping Dataset (CGD) despite having a limited
amount of labelled data by also utilizing the unlabelled data. This claim has
been validated by testing the model on images, which are not available in the
CGD. Along with this, we augment the Generative Grasping Convolutional Neural
Network (GGCNN) architecture with the decoder structure used in the VQ-VAE
model with the intuition that it should help to regress in the vector-quantized
latent space. Subsequently, the model performs significantly better than the
existing approaches which do not make use of unlabelled images to improve the
grasp.
