EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices
Overview
The paper introduces EgoKit, a toolkit designed to unify low-cost egocentric data collection across six heterogeneous host devices. It addresses fragmentation in current practices by providing a consistent recording workflow and uniform log format regardless of the device used. The system supports synchronized ego-view and wrist-view capture, with additional logging of head pose and 26-joint hand tracking on XR headsets. Validation results show that policies trained on EgoKit data perform comparably to those trained on Project Aria recordings in simulated environments.
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Data origin: human egocentric
“EgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices”
Exact source · revision 1Source owner not reported
Reported quantity · demonstrations: 41 other · Basis not reported
“abrication. To validate that EgoKit data can train robot policies, we replicated HumanEgo with 41 demonstrations recorded by EgoKit-PICO: in simulation, policies trained on 20 or 40 of them placed a cube on a tray in 60-74% of 100 unseen layouts, about as often as policies trained on Project Aria recordings of the same layouts. EgoKit is available at https://egokit.chuange.org/.”
Exact source · revision 1Source owner not reported
Reported quantity · layouts: 100 other · Basis not reported
“abrication. To validate that EgoKit data can train robot policies, we replicated HumanEgo with 41 demonstrations recorded by EgoKit-PICO: in simulation, policies trained on 20 or 40 of them placed a cube on a tray in 60-74% of 100 unseen layouts, about as often as policies trained on Project Aria recordings of the same layouts. EgoKit is available at https://egokit.chuange.org/.”
Exact source · revision 1Source owner not reported
Dataset · embodiments: Android phone · Differing source assertions
“arXiv:2605.16797v2 Announce Type: replace-cross Abstract: 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 acc”
Exact source · revision 1Source owner not reported
Dataset · embodiments: iPhone · Differing source assertions
“arXiv:2605.16797v2 Announce Type: replace-cross Abstract: 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 acc”
Exact source · revision 1Source owner not reported
Dataset · embodiments: iPad · Differing source assertions
“arXiv:2605.16797v2 Announce Type: replace-cross Abstract: 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 acc”
Exact source · revision 1Source owner not reported
Dataset · embodiments: smart glasses · Differing source assertions
“arXiv:2605.16797v2 Announce Type: replace-cross Abstract: 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 acc”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalEgoKit: Towards Unified Low-Cost Egocentric Data Collection with Heterogeneous Devices
The paper introduces EgoKit, a toolkit designed to unify low-cost egocentric data collection across six heterogeneous host devices. It addresses fragmentation in current practices by providing a consistent recording workflow and uniform log format regardless of the device used. The system supports synchronized ego-view and wrist-view capture, with additional logging of head pose and 26-joint hand tracking on XR headsets. Validation results show that policies trained on EgoKit data perform comparably to those trained on Project Aria recordings in simulated environments.
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