Speeding up 6-DoF Grasp Sampling with Quality-Diversity

arXiv:2403.06173v1 Announce Type: cross
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 …

abstract advances arxiv challenge cs.lg cs.ro data diversity generative generative models grasping however language natural natural language optimization planning quality results robotic robotic learning sampling type