让机械手学会物理感知抓取任意物体，性能领先现有方法。
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness
- 将物理约束融入扩散模型的训练与采样过程
- 在近全量开放数据集上达到顶尖抓取性能
- 构建超340万姿态的多样化抓取数据集，助力通用抓取研究
能抓取任意物体的灵巧手对通用具身智能机器人的发展至关重要。然而，由于灵巧手自由度高、物体种类繁多，鲁棒地生成高质量可用抓取姿态仍是一大挑战。本文提出DexGrasp Anything，通过在基于扩散模型的生成过程中同时整合物理约束，显著提升抓取质量，在几乎所有公开数据集上均达当前最优性能。此外，我们构建了一个新的灵巧抓取数据集，包含超过3.4百万种不同物体（超过15,000个）的多样抓取姿态，充分展现了其推动通用灵巧抓取发展的潜力。该方法与数据集代码将很快公开。
原文摘要 · Abstract (English)
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.
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