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Open-AoE is an innovative egocentric manipulation dataset and toolchain designed to facilitate embodied learning by integrating low-cost video capture with structured annotations and reusable tools. It comprises approximately 2,000 hours of manipulation video collected from over 500 contributors using smartphones, along with detailed annotations including hand poses and camera trajectories. The comprehensive processing pipeline and downstream tools significantly lower the barriers for data contribution and reuse, enabling advancements in robot learning and human-to-robot transfer.
Open-AoE transforms egocentric video capture into a powerful resource for embodied intelligence, making it easier than ever to train robots with human-like manipulation skills.
Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-level structured annotations, and reusable tools for robot learning. We present Open-AoE, an open, community-oriented egocentric manipulation dataset and toolchain spanning the full pipeline from smartphone capture to model training. Its first release contains approximately 2,000 hours of manipulation video collected in natural environments by 500+ contributors using 400+ smartphones. The dataset provides text annotations, MANO-based hand poses, camera trajectories, and temporally localized atomic actions. Open-AoE further includes a data processing pipeline that transforms raw recordings into structured samples through temporal action segmentation, semantic annotation, hand reconstruction, and camera trajectory reconstruction. Meanwhile, we provide a separate downstream toolchain supports visualization, cross-embodiment retargeting, model-specific data conversion, and training recipes for VLA policies, WAMs, and World Models. By integrating scalable capture, structured processing, and downstream adaptation, Open-AoE reduces the barriers to both data contribution and reuse, providing practical open infrastructure for embodied model training, human-to-robot transfer, and world modeling.
Microsoft Research