  Intelligent robot grasping is a very challenging task due to its inherent
complexity and non availability of sufficient labelled data. Since making
suitable labelled data available for effective training for any deep learning
based model including deep reinforcement learning is so crucial for successful
grasp learning, in this paper we propose to solve the problem of generating
grasping Poses/Rectangles using a Pix2Pix Generative Adversarial Network
(Pix2Pix GAN), which takes an image of an object as input and produces the
grasping rectangle tagged with the object as output. Here, we have proposed an
end-to-end grasping rectangle generating methodology and embedding it to an
appropriate place of an object to be grasped. We have developed two modules to
obtain an optimal grasping rectangle. With the help of the first module, the
pose (position and orientation) of the generated grasping rectangle is
extracted from the output of Pix2Pix GAN, and then the extracted grasp pose is
translated to the centroid of the object, since here we hypothesize that like
the human way of grasping of regular shaped objects, the center of
mass/centroids are the best places for stable grasping. For other irregular
shaped objects, we allow the generated grasping rectangles as it is to be fed
to the robot for grasp execution. The accuracy has significantly improved for
generating the grasping rectangle with limited number of Cornell Grasping
Dataset augmented by our proposed approach to the extent of 87.79%. Experiments
show that our proposed generative model based approach gives the promising
results in terms of executing successful grasps for seen as well as unseen
objects.
