Dexterous grasping aims to produce diverse grasping postures with a high grasping success rate. Regression-based methods that directly predict grasping parameters given the object may achieve a high success rate but often lack diversity. Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information. To mitigate, we introduce a unified diffusion-based dexterous grasp generation model, dubbed the name UGG, which operates within the object point cloud and hand parameter spaces. Our all-transformer architecture unifies the information from the object, the hand, and the contacts, introducing a novel representation of contact points for improved contact modeling. The flexibility and quality of our model enable the integration of a lightweight discriminator, benefiting from simulated discriminative data, which pushes for a high success rate while preserving high diversity. Beyond grasp generation, our model can also generate objects based on hand information, offering valuable insights into object design and studying how the generative model perceives objects. Our model achieves state-of-the-art dexterous grasping on the large-scale DexGraspNet dataset while facilitating human-centric object design, marking a significant advancement in dexterous grasping research. Our project page is https://jiaxin-lu.github.io/ugg/ .

翻译：灵巧抓取旨在生成具有高成功率的多样化抓取姿态。基于回归的方法直接预测给定物体的抓取参数，虽能实现高成功率，但往往缺乏多样性。基于生成的方法根据物体条件生成抓取姿态，通常可产生多样化抓取，但因缺乏判别性信息而难以保证高成功率。为此，我们提出一种基于扩散的统一化灵巧抓取生成模型UGG，该模型在物体点云与手部参数空间中运行。我们采用全Transformer架构统一整合物体、手部及接触信息，并引入新颖的接触点表征以改进接触建模。模型的灵活性与高质量使我们能集成轻量级判别器，借助仿真判别数据，在保持高多样性的同时提升成功率。除抓取生成外，我们的模型还可根据手部信息生成物体，为物体设计提供重要见解，并揭示生成模型对物体的感知方式。本模型在大规模DexGraspNet数据集上实现了当前最优的灵巧抓取性能，同时支持以人为中心的物体设计，标志着灵巧抓取研究的重大进步。项目页面：https://jiaxin-lu.github.io/ugg/ 。