A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexterous hands and the vast diversity of objects, generating high-quality, usable grasping poses in a robust manner is a significant challenge. In this paper, we introduce DexGrasp Anything, a method that effectively integrates physical constraints into both the training and sampling phases of a diffusion-based generative model, achieving state-of-the-art performance across nearly all open datasets. Additionally, we present a new dexterous grasping dataset containing over 3.4 million diverse grasping poses for more than 15k different objects, demonstrating its potential to advance universal dexterous grasping. The code of our method and our dataset will be publicly released soon.

本研究解决了通用智能机器人在灵巧抓取中的高自由度与物体多样性所带来的挑战。通过将物理约束有效融入扩散式生成模型的训练和采样阶段，提出了DexGrasp Anything方法，并在几乎所有公开数据集上实现了先进性能。同时，推出了包含超过340万种多样抓取姿态和15000种不同物体的新灵巧抓取数据集，为推动通用灵巧抓取展现了潜力。

DexGrasp 一切：朝着具有物理意识的通用机器人灵巧抓取迈进