Dex1B：利用10亿条演示进行灵巧操作学习
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
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中文总结 AI 辅助
本文提出Dex1B，一个包含10亿条演示的灵巧操作数据集，通过集成几何约束的生成模型构建，在仿真和真实机器人实验中显著优于现有方法。
中文摘要 AI 辅助
生成大规模灵巧手操作演示仍然具有挑战性，近年来已提出多种方法来解决这一问题。其中，生成模型已成为一种有前景的范式，能够高效地创建多样化且物理上合理的演示。在本文中，我们介绍了Dex1B，一个由生成模型产生的大规模、多样化且高质量的演示数据集。该数据集包含针对两个基本任务（抓取和关节运动）的10亿条演示。为了构建该数据集，我们提出了一种集成几何约束以提高可行性的生成模型，并应用额外条件来增强多样性。我们在既有和新引入的仿真基准上验证了该模型，其性能显著优于先前的最先进方法。此外，我们通过真实世界机器人实验证明了其有效性和鲁棒性。我们的项目页面位于https://jianglongye.com/dex1b。
英文摘要
Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, generative models have emerged as a promising paradigm, enabling the efficient creation of diverse and physically plausible demonstrations. In this paper, we introduce Dex1B, a large-scale, diverse, and high-quality demonstration dataset produced with generative models. The dataset contains one billion demonstrations for two fundamental tasks: grasping and articulation. To construct it, we propose a generative model that integrates geometric constraints to improve feasibility and applies additional conditions to enhance diversity. We validate the model on both established and newly introduced simulation benchmarks, where it significantly outperforms prior state-of-the-art methods. Furthermore, we demonstrate its effectiveness and robustness through real-world robot experiments. Our project page is at https://jianglongye.com/dex1b
发表机构
- UC San Diego（UC圣地亚哥大学）
机构由 AI 辅助整理，请以论文原文为准。
