EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks

TL;DR

EgoLive is a large-scale, high-quality egocentric dataset capturing real-world human routines, designed to advance robot manipulation learning with diverse, ecological, and annotated data collected in natural scenarios.

Contribution

The paper introduces EgoLive, the largest open-source egocentric dataset with high-quality annotations, collected in unconstrained real-world environments for robot learning.

Findings

EgoLive is the largest annotated egocentric dataset for real-world tasks.

Data quality is enhanced by a custom head-mounted capture device.

The dataset covers diverse practical scenarios like home service and retail.

Abstract

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.