用质量多样性算法生成大规模抓取数据集，提升效率与多样性
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity
- 基于质量多样性算法优化抓取采样，结合物体网格变换与迁移学习
- 每发现一个稳健抓取姿态所需评估次数减少最多20%
- 生成的QDGset数据集含3.5倍更多抓取姿势和4.5倍更多物体
近期人工智能进展推动了机器人学习的突破，但抓取等技能仍未完全解决。许多研究利用合成抓取数据集学习未知物体的抓取方法，但这些数据集多采用简单采样策略与先验知识生成。质量-多样性（QD）算法已被证明能显著提升抓取采样的效率。本文将QDG-6DoF这一面向对象中心抓取的姿态生成框架扩展，用于大规模合成抓取数据集生产。提出一种数据增强方法，结合物体网格变换与先前抓取库的迁移学习。实验表明，该方法可使每个新发现稳健抓取姿态所需的评估次数减少最多20%。利用此方法生成了QDGset数据集，其包含约3.5倍于当前最优水平的6DoF抓取姿态，以及4.5倍以上的物体数量。本方法支持用户便捷生成数据，有望推动合成抓取数据的大规模协作建设。
原文摘要 · Abstract (English)
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.
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