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
this https URL .

通过在物体点云和手部参数空间中操作，我们引入了一个统一的基于扩散的灵巧抓取生成模型 (UGG)，该模型统一了物体、手部和接触点的信息，引入了一种改进接触建模的新颖表示。我们的模型灵活性和质量使得我们能够集成一个轻量级的鉴别器，通过模拟鉴别数据来推动高成功率同时保持高多样性。除了抓取生成，我们的模型还可以根据手部信息生成物体，为对象设计和研究生成模型的感知提供了宝贵的洞察。我们的模型在大规模的 DexGraspNet 数据集上实现了最先进的灵巧抓取，并促进了以人为中心的对象设计，标志着灵巧抓取研究的重要进展。