Speeding up 6-DoF Grasp Sampling with Quality-Diversity
Johann Huber François Hélénon Mathilde Kappel Elie Chelly Mahdi Khoramshahi Faïz Ben Amar Stéphane Doncieux
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.