Recent advances in AI have led to significant results in robotic learning, but skills like grasping remain partially solved. Many recent works exploit synthetic grasping datasets to learn to grasp unknown objects. However, those datasets were generated using simple grasp sampling methods using priors. Recently, Quality-Diversity (QD) algorithms have been proven to make grasp sampling significantly more efficient. In this work, we extend QDG-6DoF, a QD framework for generating object-centric grasps, to scale up the production of synthetic grasping datasets. We propose a data augmentation method that combines the transformation of object meshes with transfer learning from previous grasping repertoires. The conducted experiments show that this approach reduces the number of required evaluations per discovered robust grasp by up to 20%. We used this approach to generate QDGset, a dataset of 6DoF grasp poses that contains about 3.5 and 4.5 times more grasps and objects, respectively, than the previous state-of-the-art. Our method allows anyone to easily generate data, eventually contributing to a large-scale collaborative dataset of synthetic grasps.

翻译：人工智能的最新进展已在机器人学习领域取得显著成果，但诸如抓取等技能仍未得到完全解决。许多近期研究利用合成抓取数据集来学习抓取未知物体。然而，这些数据集通常采用基于先验的简单抓取采样方法生成。近期研究表明，质量多样性算法能显著提升抓取采样效率。本研究扩展了面向物体中心抓取生成的QD框架QDG-6DoF，以实现合成抓取数据集的大规模生产。我们提出一种数据增强方法，将物体网格变换与既往抓取技能库的迁移学习相结合。实验表明，该方法可将每个已发现鲁棒抓取所需的评估次数减少达20%。基于此方法，我们构建了QDGset数据集——该6自由度抓取姿态数据集包含的抓取样本与物体数量分别达到先前最优水平的约3.5倍和4.5倍。本方法使研究者能够便捷地生成数据，最终为构建大规模协作式合成抓取数据集作出贡献。