  Multi-finger grasping relies on high quality training data, which is hard to
obtain: human data is hard to transfer and synthetic data relies on simplifying
assumptions that reduce grasp quality. By making grasp simulation
differentiable, and contact dynamics amenable to gradient-based optimization,
we accelerate the search for high-quality grasps with fewer limiting
assumptions. We present Grasp'D-1M: a large-scale dataset for multi-finger
robotic grasping, synthesized with Fast- Grasp'D, a novel differentiable
grasping simulator. Grasp'D- 1M contains one million training examples for
three robotic hands (three, four and five-fingered), each with multimodal
visual inputs (RGB+depth+segmentation, available in mono and stereo). Grasp
synthesis with Fast-Grasp'D is 10x faster than GraspIt! and 20x faster than the
prior Grasp'D differentiable simulator. Generated grasps are more stable and
contact-rich than GraspIt! grasps, regardless of the distance threshold used
for contact generation. We validate the usefulness of our dataset by retraining
an existing vision-based grasping pipeline on Grasp'D-1M, and showing a
dramatic increase in model performance, predicting grasps with 30% more
contact, a 33% higher epsilon metric, and 35% lower simulated displacement.
Additional details at https://dexgrasp.github.io.
