  Generating grasp poses is a crucial component for any robot object
manipulation task. In this work, we formulate the problem of grasp generation
as sampling a set of grasps using a variational autoencoder and assess and
refine the sampled grasps using a grasp evaluator model. Both Grasp Sampler and
Grasp Refinement networks take 3D point clouds observed by a depth camera as
input. We evaluate our approach in simulation and real-world robot experiments.
Our approach achieves 88\% success rate on various commonly used objects with
diverse appearances, scales, and weights. Our model is trained purely in
simulation and works in the real world without any extra steps. The video of
our experiments can be found at:
https://research.nvidia.com/publication/2019-10_6-DOF-GraspNet\%3A-Variational
