Dexterous functional tool-use grasping is essential for effective robotic manipulation of tools. However, existing approaches face significant challenges in efficiently constructing large-scale datasets and ensuring generalizability to everyday object scales. These issues primarily arise from size mismatches between robotic and human hands, and the diversity in real-world object scales. To address these limitations, we propose the ScaleADFG framework, which consists of a fully automated dataset construction pipeline and a lightweight grasp generation network. Our dataset introduce an affordance-based algorithm to synthesize diverse tool-use grasp configurations without expert demonstrations, allowing flexible object-hand size ratios and enabling large robotic hands (compared to human hands) to grasp everyday objects effectively. Additionally, we leverage pre-trained models to generate extensive 3D assets and facilitate efficient retrieval of object affordances. Our dataset comprising five object categories, each containing over 1,000 unique shapes with 15 scale variations. After filtering, the dataset includes over 60,000 grasps for each 2 dexterous robotic hands. On top of this dataset, we train a lightweight, single-stage grasp generation network with a notably simple loss design, eliminating the need for post-refinement. This demonstrates the critical importance of large-scale datasets and multi-scale object variant for effective training. Extensive experiments in simulation and on real robot confirm that the ScaleADFG framework exhibits strong adaptability to objects of varying scales, enhancing functional grasp stability, diversity, and generalizability. Moreover, our network exhibits effective zero-shot transfer to real-world objects. Project page is available at https://sizhe-wang.github.io/ScaleADFG_webpage

翻译：灵巧的功能性工具使用抓取对于机器人有效操作工具至关重要。然而，现有方法在高效构建大规模数据集以及确保对日常物体尺度的泛化能力方面面临显著挑战。这些问题主要源于机器人手与人类手之间的尺寸不匹配，以及现实世界中物体尺度的多样性。为应对这些局限，我们提出了ScaleADFG框架，该框架包含一个全自动数据集构建流程和一个轻量级抓取生成网络。我们的数据集引入了一种基于可供性的算法，无需专家演示即可合成多样化的工具使用抓取配置，允许灵活的对象-手尺寸比例，并使大型机器人手（相较于人手）能够有效抓取日常物体。此外，我们利用预训练模型生成大量3D资产，并促进对象可供性的高效检索。我们的数据集包含五个对象类别，每个类别包含超过1000个独特形状，每个形状有15种尺度变体。经过筛选，该数据集为两种灵巧机器人手各包含超过60,000个抓取样本。基于此数据集，我们训练了一个轻量级的单阶段抓取生成网络，其损失函数设计极为简洁，无需后处理精修。这证明了大规模数据集和多尺度物体变体对于有效训练的关键重要性。在仿真和真实机器人上进行的大量实验证实，ScaleADFG框架对不同尺度的物体表现出强大的适应性，提升了功能性抓取的稳定性、多样性和泛化能力。此外，我们的网络对现实世界物体展现出有效的零样本迁移能力。项目页面详见：https://sizhe-wang.github.io/ScaleADFG_webpage