  Gathering real-world data from the robot quickly becomes a bottleneck when
constructing a robot learning system for grasping. In this work, we design a
semi-supervised grasping system that, on top of a small sample of robot
experience, takes advantage of images of products to be picked, which are
collected without any interactions with the robot. We validate our findings
both in the simulation and in the real world. In the regime of a small number
of robot training samples, taking advantage of the unlabeled data allows us to
achieve performance at the level of 10-fold bigger dataset size used by the
baseline. The code and datasets used in the paper will be released at
https://github.com/nomagiclab/grasping-student.
