  Recent advances in AI have led to significant results in robotic learning,
including natural language-conditioned planning and efficient optimization of
controllers using generative models. However, the interaction data remains the
bottleneck for generalization. Getting data for grasping is a critical
challenge, as this skill is required to complete many manipulation tasks.
Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse,
high-performing solutions to a given problem. This paper investigates how QD
can be combined with priors to speed up the generation of diverse grasps poses
in simulation compared to standard 6-DoF grasp sampling schemes. Experiments
conducted on 4 grippers with 2-to-5 fingers on standard objects show that QD
outperforms commonly used methods by a large margin. Further experiments show
that QD optimization automatically finds some efficient priors that are usually
hard coded. The deployment of generated grasps on a 2-finger gripper and an
Allegro hand shows that the diversity produced maintains sim-to-real
transferability. We believe these results to be a significant step toward the
generation of large datasets that can lead to robust and generalizing robotic
grasping policies.
