  Most state-of-the-art data-driven grasp sampling methods propose stable and
collision-free grasps uniformly on the target object. For bin-picking,
executing any of those reachable grasps is sufficient. However, for completing
specific tasks, such as squeezing out liquid from a bottle, we want the grasp
to be on a specific part of the object's body while avoiding other locations,
such as the cap. This work presents a generative grasp sampling network, VCGS,
capable of constrained 6 Degrees of Freedom (DoF) grasp sampling. In addition,
we also curate a new dataset designed to train and evaluate methods for
constrained grasping. The new dataset, called CONG, consists of over 14 million
training samples of synthetically rendered point clouds and grasps at random
target areas on 2889 objects. VCGS is benchmarked against GraspNet, a
state-of-the-art unconstrained grasp sampler, in simulation and on a real
robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success
rate than the baseline while being 2-3 times as sample efficient. Supplementary
material is available on our project website.
