EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices
For researchers in robot learning, activity understanding, and embodied AI, EgoKit reduces the fragmentation and engineering overhead of multi-device egocentric data collection, though it is an incremental tool rather than a novel method.
EgoKit provides a unified toolkit for egocentric data collection across six heterogeneous devices (Android, iPhone, iPad, smart glasses, XR headsets), enabling synchronized ego-view and wrist-view capture with a consistent recording workflow and uniform log format, without requiring custom hardware fabrication.
Egocentric video is increasingly used as a data source for robot learning, activity understanding, and embodied AI research, but collecting it at scale remains fragmented in practice: each candidate host device, such as an Android phone, iPhone, iPad, smart glasses, or extended reality (XR) headset, exposes a different SDK, a different policy on raw camera access, and different limitations on external USB cameras and on-device tracking. Synchronized ego-view and wrist-view capture is therefore typically obtained by either committing to a single proprietary platform or building one-off rigs that do not transfer across devices. To address this gap, we present EgoKit, a toolkit that exposes the same egocentric recording workflow across six heterogeneous host devices. Across all supported devices, EgoKit presents the same recording interaction and produces locally stored video with a uniform log format; on XR headsets, it additionally logs head pose and OpenXR-standard 26-joint hand tracking aligned to the video streams. The companion accessories, including two wrist cameras with mounts, a head strap, and a USB-C hub, add wrist-view capture to any supported host without custom hardware fabrication. EgoKit is available at \url{https://egokit.chuange.org/}.