构建全球协作的机器人学习数据平台，用人类操作视频提升机器人训练效果。
EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
- 搭建统一平台，整合全球人类操作数据的采集与共享
- 提供80,000段任务演示，覆盖1,965项任务和2,087名不同操作者
- 支持多实验室复现实验，推动可重复的人类数据迁移研究
机器人学习依赖大规模多样化数据，但机器人数据采集成本高且难以扩展。以第一人称视角记录的人类操作数据为解决此问题提供了新途径，但现有数据集常受限于范围、难拓展且分散于各机构。我们提出EgoVerse，一个面向人类数据驱动机器人学习的协作平台，统一数据采集、处理与访问流程，支持个人研究者、学术实验室及产业伙伴共同贡献。当前版本包含1,362小时（80,000个轨迹）的人类示范，覆盖1,965项任务、240个场景和2,087名不同演示者，具备标准化格式、与操作相关的标注及下游学习工具。此外，我们开展跨多实验室、多任务、多机器人形态的大规模人类到机器人迁移研究，发现策略性能随人类数据量增加而提升，但有效扩展依赖于人类数据与机器人目标的一致性。该数据集、平台与研究共同奠定了可复现的人类数据驱动机器人学习基础。更多内容见https://egoverse.ai/
原文摘要 · Abstract (English)
Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human data offer a promising alternative by capturing rich manipulation behavior across everyday environments. However, existing human datasets are often limited in scope, difficult to extend, and fragmented across institutions. We introduce EgoVerse, a collaborative platform for human data-driven robot learning that unifies data collection, processing, and access under a shared framework, enabling contributions from individual researchers, academic labs, and industry partners. The current release includes 1,362 hours (80k episodes) of human demonstrations spanning 1,965 tasks, 240 scenes, and 2,087 unique demonstrators, with standardized formats, manipulation-relevant annotations, and tooling for downstream learning. Beyond the dataset, we conduct a large-scale study of human-to-robot transfer with experiments replicated across multiple labs, tasks, and robot embodiments under shared protocols. We find that policy performance generally improves with increased human data, but that effective scaling depends on alignment between human data and robot learning objectives. Together, the dataset, platform, and study establish a foundation for reproducible progress in human data-driven robot learning. Videos and additional information can be found at https://egoverse.ai/
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