  We present an end-to-end algorithm for training deep neural networks to grasp
novel objects. Our algorithm builds all the essential components of a grasping
system using a forward-backward automatic differentiation approach, including
the forward kinematics of the gripper, the collision between the gripper and
the target object, and the metric for grasp poses. In particular, we show that
a generalized Q1 grasp metric is defined and differentiable for inexact grasps
generated by a neural network, and the derivatives of our generalized Q1 metric
can be computed from a sensitivity analysis of the induced optimization
problem. We show that the derivatives of the (self-)collision terms can be
efficiently computed from a watertight triangle mesh of low-quality.
Altogether, our algorithm allows for the computation of grasp poses for
high-DOF grippers in an unsupervised mode with no ground truth data, or it
improves the results in a supervised mode using a small dataset. Our new
learning algorithm significantly simplifies the data preparation for
learning-based grasping systems and leads to higher qualities of learned grasps
on common 3D shape datasets [7, 49, 26, 25], achieving a 22% higher success
rate on physical hardware and a 0.12 higher value on the Q1 grasp quality
metric.
