Large-scale pre-training is fundamental for generalization in language and vision models, but data for dexterous hand manipulation remains limited in scale and diversity, hindering policy generalization. Limited scenario diversity, misaligned modalities, and insufficient benchmarking constrain current human manipulation datasets. To address these gaps, we introduce World In Your Hands (WiYH), a large-scale open-source ecosystem for human-centric manipulation learning. WiYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WiYH Dataset, featuring over 1,000 hours of multi-modal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WiYH ecosystem show that integrating WiYH's human-centric data significantly enhances the generalization and robustness of dexterous hand policies in tabletop manipulation tasks. We believe that World In Your Hands will bring new insights into human-centric data collection and policy learning to the community.

翻译：大规模预训练是语言和视觉模型实现泛化的基础，但灵巧手部操控的数据在规模和多样性上仍然有限，这阻碍了策略的泛化能力。当前的人类操控数据集受限于场景多样性不足、模态未对齐以及基准测试不充分。为弥补这些不足，我们推出了"世界在你手中"（WiYH），一个用于以人为中心的操控学习的大规模开源生态系统。WiYH包含：（1）Oracle套件，一个带有自动标注流程的可穿戴数据采集工具包，用于精确的运动捕捉；（2）WiYH数据集，包含超过1000小时的多模态操控数据，涵盖数百种技能及多样化的真实世界场景；（3）支持从感知到动作任务的广泛标注和基准测试。此外，基于WiYH生态系统的实验表明，整合WiYH以人为中心的数据能显著增强灵巧手策略在桌面操控任务中的泛化能力和鲁棒性。我们相信，"世界在你手中"将为学界带来关于以人为中心的数据采集和策略学习的新见解。