The advancement of robot learning is currently hindered by the scarcity of large-scale, high-quality datasets. While established data collection methods such as teleoperation and universal manipulation interfaces dominate current datasets, they suffer from inherent limitations in scalability and real-world deployability. Human egocentric video collection, by contrast, has emerged as a promising approach to enable scalable, natural and in-the-wild data collection. As such, we present EgoLive, a large-scale, high-quality egocentric dataset designed explicitly for robot manipulation learning. EgoLive establishes three distinctive technical advantages over existing egocentric datasets: first, it represents the largest open-source annotated egocentric dataset focused on real-world task-oriented human routines to date; second, it delivers leading data quality via a customized head-mounted capture device and comprehensive high-precision multi-modal annotations; third, all data is collected exclusively in unconstrained real-world scenarios and encompasses vertical field human working data, including home service, retail, and other practical work scenarios, providing superior diversity and ecological validity. With the introduction of EgoLive, we aim to provide the research community with a scalable, high-quality dataset that accelerates breakthroughs in generalizable robotic models and facilitates the real-world deployment of robot systems.
@article{arxiv.2604.23570,
title = {EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks},
author = {Yihang Li and Xuelong Wei and Jingzhou Luo and Yingjing Xiao and Yibo Bai and Guangyuan Zhou and Teng Zou and Chenguang Gui and Jiajun Wen and He Zhang and Kangliang Chen and Xing Pan and Shuaiyan Liu and Daming Wang and Tao An and Jiayi Li and Shibo Jin and Wanwan Zhang and Tianyu Wang and Boren Wei and Zhixuan Huang and Fangsheng Liu and Ruodai Li and Hui Zhang and Anson Li and Yicheng Gong and Peng Cao and Jiaming Liang and Liang Lin},
journal= {arXiv preprint arXiv:2604.23570},
year = {2026}
}