Guides
Guides for collecting and training on egocentric data
Standalone, practical write-ups of the things teams ask before a capture program: how to spec a skill, how sync is measured, how much data a policy actually needs, and how to license it for commercial use.
- How to write a skill spec for egocentric data collectionName the task, geometry, condition budget, reject rule and one acceptance test.
- How to measure cross-stream sync error in egocentric captureHardware-trigger every stream onto one PTP clock, then measure the residual offset.
- Camera calibration procedure for a multi-camera egocentric rigOpening and closing solves, sub-0.28 px reprojection, drift measured per episode.
- Evaluation methodology for egocentric manipulation datasetsHeld-out environments, matched hour budgets, and an open evaluation protocol.
- Python quickstart for loading egocentric training dataInstall, list access, pull an episode, and stream aligned frames into training.
- How much egocentric data do you need to train a robot policy?Start around 50–100 validated hours per skill, then scale where evaluation is thin.
- Egocentric data for humanoid robots: what to collectFirst-person, bimanual demonstration with 3D hand pose maps cleanly to humanoids.
- How to license training data for commercial modelsCheck consent documentation and for a perpetual, buyer-owned commercial license.
- How to blur faces in video automaticallyDetect each face as a box, track it across frames, blur it into the pixels, then review and certify.
- Face detection and bounding boxes, explainedWhat a detector returns, how the confidence threshold works, and why it is only step one.
- Video anonymization and privacy law: GDPR, CCPA and de-identificationWhat counts as personal data in footage, and why anonymization must be irreversible to fall out of scope.
Every guide describes the schema in the sample pack.
Download 40 episodes across 4 environments and check the claims against real data.