  Grasping made impressive progress during the last few years thanks to deep
learning. However, there are many objects for which it is not possible to
choose a grasp by only looking at an RGB-D image, might it be for physical
reasons (e.g., a hammer with uneven mass distribution) or task constraints
(e.g., food that should not be spoiled). In such situations, the preferences of
experts need to be taken into account.
  In this paper, we introduce a data-efficient grasping pipeline (Latent Space
GP Selector -- LGPS) that learns grasp preferences with only a few labels per
object (typically 1 to 4) and generalizes to new views of this object. Our
pipeline is based on learning a latent space of grasps with a dataset generated
with any state-of-the-art grasp generator (e.g., Dex-Net). This latent space is
then used as a low-dimensional input for a Gaussian process classifier that
selects the preferred grasp among those proposed by the generator.
  The results show that our method outperforms both GR-ConvNet and GG-CNN (two
state-of-the-art methods that are also based on labeled grasps) on the Cornell
dataset, especially when only a few labels are used: only 80 labels are enough
to correctly choose 80% of the grasps (885 scenes, 244 objects). Results are
similar on our dataset (91 scenes, 28 objects).
