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抓取成功率的多样化抓握姿态。基于回归的方法在给定物体时直接预测抓取参数，可能实现高成功率，但往往缺乏多样性。基于生成的方法根据物体条件生成抓握姿态，通常能产生多样化的抓取，但由于缺乏判别性信息，难以保证高抓取成功率。为缓解此问题，我们提出了一种统一的基于扩散的灵巧抓取生成模型，命名为UGG，该模型在物体点云和手部参数空间内运行。我们的全Transformer架构统一了来自物体、手部和接触点的信息，引入了一种新颖的接触点表示以改进接触建模。模型的灵活性和高质量特性使得集成轻量级判别器成为可能，该判别器受益于模拟的判别性数据，从而在保持高多样性的同时推动高成功率。除了抓取生成，我们的模型还能基于手部信息生成物体，为物体设计提供了宝贵见解，并有助于研究生成模型如何感知物体。我们的模型在大规模DexGraspNet数据集上实现了最先进的灵巧抓取性能，同时促进了以人为中心的物体设计，标志着灵巧抓取研究的重要进展。项目页面为 https://jiaxin-lu.github.io/ugg/。