Speeding up 6-DoF Grasp Sampling with Quality-Diversity

Understand

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.