  Vacuum-based end effectors are widely used in industry and are often
preferred over parallel-jaw and multifinger grippers due to their ability to
lift objects with a single point of contact. Suction grasp planners often
target planar surfaces on point clouds near the estimated centroid of an
object. In this paper, we propose a compliant suction contact model that
computes the quality of the seal between the suction cup and local target
surface and a measure of the ability of the suction grasp to resist an external
gravity wrench. To characterize grasps, we estimate robustness to perturbations
in end-effector and object pose, material properties, and external wrenches. We
analyze grasps across 1,500 3D object models to generate Dex-Net 3.0, a dataset
of 2.8 million point clouds, suction grasps, and grasp robustness labels. We
use Dex-Net 3.0 to train a Grasp Quality Convolutional Neural Network (GQ-CNN)
to classify robust suction targets in point clouds containing a single object.
We evaluate the resulting system in 350 physical trials on an ABB YuMi fitted
with a pneumatic suction gripper. When evaluated on novel objects that we
categorize as Basic (prismatic or cylindrical), Typical (more complex
geometry), and Adversarial (with few available suction-grasp points) Dex-Net
3.0 achieves success rates of 98$\%$, 82$\%$, and 58$\%$ respectively,
improving to 81$\%$ in the latter case when the training set includes only
adversarial objects. Code, datasets, and supplemental material can be found at
http://berkeleyautomation.github.io/dex-net .
