DexGrasp Anything：迈向具有物理感知的通用机器人灵巧抓取
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness
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中文总结 AI 辅助
针对灵巧手高自由度与物体多样导致的抓取位姿生成难题，提出 DexGrasp Anything，在扩散模型训练与采样中融入物理约束，并发布大规模抓取数据集，实现通用灵巧抓取的先进性能。
中文摘要 AI 辅助
能够抓取任何物体的灵巧手对于开发通用具身智能机器人至关重要。然而，由于灵巧手的高自由度以及物体的巨大多样性，以鲁棒的方式生成高质量、可用的抓取位姿是一项重大挑战。在本文中，我们提出了 DexGrasp Anything，该方法将物理约束有效整合到基于扩散的生成模型的训练和采样两个阶段中，在几乎所有开放数据集上均取得了最先进的性能。此外，我们还提出了一个新的灵巧抓取数据集，其中包含针对超过 15k 个不同物体的 340 万余个多样化抓取位姿，展示了其推动通用灵巧抓取发展的潜力。我们方法的代码和数据集将很快公开发布。
英文摘要
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.
发表机构
- ShanghaiTech University（上海科技大学）
机构由 AI 辅助整理，请以论文原文为准。
