Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
This addresses the problem of limited demonstration data for dexterous manipulation in robotics, though it is incremental as it builds on existing generative model approaches.
The authors tackled the challenge of generating large-scale demonstrations for dexterous hand manipulation by introducing Dex1B, a dataset of one billion demonstrations for grasping and articulation tasks, which significantly outperformed prior state-of-the-art methods in benchmarks and real-world experiments.
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