MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware

Abstract

Vision-language-action (VLA) models have driven demand for large-scale egocentric datasets, yet the hardware and infrastructure to collect long-horizon data remain inaccessible. Datasets today typically have episodes only a few minutes long, which fails to capture the long-horizon temporal dependencies that complex robotic task execution requires. We present MobileEgo Anywhere, a framework for collecting hour-plus egocentric trajectories on commodity mobile hardware that uses modern smartphone sensors for long-term pose tracking without the hardware barriers of traditional robotics data collection. We release three components: (1) STERA, an open-source video-processing pipeline that converts raw mobile captures into standardized, training-ready formats for VLA and foundation-model research; (2) a free mobile app that lets any user record egocentric activity; and (3) a 200-hour dataset of diverse, long-form egocentric data with persistent state tracking across 584 sessions. We further show this data is a usable training signal:mid-training a VLA on it lowers held-out action-prediction error.