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

Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation