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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23570 |
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| _version_ | 1866913062824443904 |
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| author | Li, Yihang Wei, Xuelong Luo, Jingzhou Xiao, Yingjing Bai, Yibo Zhou, Guangyuan Zou, Teng Gui, Chenguang Wen, Jiajun Zhang, He Chen, Kangliang Pan, Xing Liu, Shuaiyan Wang, Daming An, Tao Li, Jiayi Jin, Shibo Zhang, Wanwan Wang, Tianyu Wei, Boren Huang, Zhixuan Liu, Fangsheng Li, Ruodai Zhang, Hui Li, Anson Gong, Yicheng Cao, Peng Liang, Jiaming Lin, Liang |
| author_facet | Li, Yihang Wei, Xuelong Luo, Jingzhou Xiao, Yingjing Bai, Yibo Zhou, Guangyuan Zou, Teng Gui, Chenguang Wen, Jiajun Zhang, He Chen, Kangliang Pan, Xing Liu, Shuaiyan Wang, Daming An, Tao Li, Jiayi Jin, Shibo Zhang, Wanwan Wang, Tianyu Wei, Boren Huang, Zhixuan Liu, Fangsheng Li, Ruodai Zhang, Hui Li, Anson Gong, Yicheng Cao, Peng Liang, Jiaming Lin, Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23570 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks Li, Yihang Wei, Xuelong Luo, Jingzhou Xiao, Yingjing Bai, Yibo Zhou, Guangyuan Zou, Teng Gui, Chenguang Wen, Jiajun Zhang, He Chen, Kangliang Pan, Xing Liu, Shuaiyan Wang, Daming An, Tao Li, Jiayi Jin, Shibo Zhang, Wanwan Wang, Tianyu Wei, Boren Huang, Zhixuan Liu, Fangsheng Li, Ruodai Zhang, Hui Li, Anson Gong, Yicheng Cao, Peng Liang, Jiaming Lin, Liang Robotics 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. |
| title | EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.23570 |