  General robot grasping in clutter requires the ability to synthesize grasps
that work for previously unseen objects and that are also robust to physical
interactions, such as collisions with other objects in the scene. In this work,
we design and train a network that predicts 6 DOF grasps from 3D scene
information gathered from an on-board sensor such as a wrist-mounted depth
camera. Our proposed Volumetric Grasping Network (VGN) accepts a Truncated
Signed Distance Function (TSDF) representation of the scene and directly
outputs the predicted grasp quality and the associated gripper orientation and
opening width for each voxel in the queried 3D volume. We show that our
approach can plan grasps in only 10 ms and is able to clear 92% of the objects
in real-world clutter removal experiments without the need for explicit
collision checking. The real-time capability opens up the possibility for
closed-loop grasp planning, allowing robots to handle disturbances, recover
from errors and provide increased robustness. Code is available at
https://github.com/ethz-asl/vgn.
