Speeding up 6-DoF Grasp Sampling with Quality-Diversity

• URL: http://arxiv.org/abs/2403.06173v1
• Date: Sun, 10 Mar 2024 10:58:54 GMT
• Title: Speeding up 6-DoF Grasp Sampling with Quality-Diversity
• Authors: Johann Huber, Fran\c{c}ois H\'el\'enon, Mathilde Kappel, Elie Chelly, Mahdi Khoramshahi, Fa\"iz Ben Amar, St\'ephane Doncieux
• Abstract summary: Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse, high-performing solutions to a given problem. Experiments conducted on 4 grippers with 2-to-5 fingers on standard objects show that QD outperforms commonly used methods by a large margin.
• Score: 1.533848041901807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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