用生成模型打造10亿条高质抓取与操作演示数据集
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
- 基于几何约束与多样性条件的生成模型，高效构建真实可行的演示序列
- 涵盖10亿条演示，覆盖抓取与关节操作两大任务，显著超越现有方法
- 支持仿真与真实机器人验证，适合具身智能与强化学习研究者使用
生成大规模、高逼真度的手部精细操作演示仍具挑战。近年来，生成模型成为高效创建多样化且物理合理演示的有前景范式。本文提出Dex1B，一个基于生成模型构建的大规模、多样且高质量演示数据集，包含10亿条用于基础任务——抓取与关节操作的演示。为构建该数据集，我们设计了一种融合几何约束以提升可行性，并引入额外条件以增强多样性的生成模型。在既有及新提出的仿真基准上验证，该模型显著优于先前最先进方法。此外，通过真实机器人实验进一步证明了其有效性与鲁棒性。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。