Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers, partially due to the lack of a large-scale dataset. In this work, we present a large-scale robotic dexterous grasp dataset, DexGraspNet, generated by our proposed highly efficient synthesis method that can be generally applied to any dexterous hand. Our method leverages a deeply accelerated differentiable force closure estimator and thus can efficiently and robustly synthesize stable and diverse grasps on a large scale. We choose ShadowHand and generate 1.32 million grasps for 5355 objects, covering more than 133 object categories and containing more than 200 diverse grasps for each object instance, with all grasps having been validated by the Isaac Gym simulator. Compared to the previous dataset from Liu et al. generated by GraspIt!, our dataset has not only more objects and grasps, but also higher diversity and quality. Via performing cross-dataset experiments, we show that training several algorithms of dexterous grasp synthesis on our dataset significantly outperforms training on the previous one. To access our data and code, including code for human and Allegro grasp synthesis, please visit our project page: https://pku-epic.github.io/DexGraspNet/.

翻译：机器人灵巧抓取是实现类人灵巧物体操作的第一步，因此是一项关键的机器人技术。然而，与平行夹爪的物体抓取相比，灵巧抓取的研究尚不充分，部分原因在于缺乏大规模数据集。本文提出了一个大规模机器人灵巧抓取数据集DexGraspNet，该数据集由我们提出的高效合成方法生成，可普遍适用于任何灵巧手。我们的方法利用深度加速可微力闭合估计器，从而能够高效且鲁棒地大规模合成稳定且多样化的抓取。我们选取ShadowHand，为5355个物体生成了132万个抓取，覆盖133个以上物体类别，每个物体实例包含200余种多样化抓取，且所有抓取均经过Isaac Gym模拟器验证。与先前由Liu等人使用GraspIt!生成的数据集相比，我们的数据集不仅包含更多物体和抓取，而且具有更高的多样性和质量。通过跨数据集实验，我们发现基于本数据集训练多种灵巧抓取合成算法，其性能显著优于基于先前数据集训练的结果。如需获取我们的数据和代码（包括人类和Allegro抓取合成代码），请访问项目页面：https://pku-epic.github.io/DexGraspNet/。