Demonstration capture, specified to a brief, for bimanual imitation learning, dexterous manipulation and vision-language-action policies.
A capture specification pairs first-person video with the telemetry a policy needs (IMU streams and a 3D camera trajectory), and every clip is annotated against the open FH-Ego v1 schema. Nothing here is stock: the brief defines the task, the environment and the rig before anything is recorded.
Every clip carries a SHA-256 consent receipt in a hash-chained, DPDP-aligned ledger, so a buyer re-verifies one clip instead of trusting a badge. Automated face redaction (YuNet where the host provides it, an OpenCV cascade where it does not) runs server-side at ingestion, before any human review, and fails closed; it refuses to emit a clip rather than emit one un-redacted. Licence plates are not covered. We hold no third-party attestation: no SOC 2, no ISO 27001.
Scoping is a conversation, not a price list: environment, session length, sensor rig and annotation tier each move the number. Capture Partners are paid from a published floor of ₹229/hr of accepted capture, by UPI on QA approval, no agent in the middle and no deduction. Those are the terms partners will be paid on, not a payout rail already running: the Capture Partner network is in pilot build-out and no payment provider is integrated.
Simulators leave a sim-to-real gap, and static web video carries no spatial depth, no IMU and no camera calibration, which is why a robotics brief starts from purpose-built capture rather than a scrape. A brief names its own environment, domestic through industrial to healthcare, and its own rig, from gaze-oriented wearables to global-shutter stereo head rigs.
Every format below is verified by an automated export bench before we quote it. MCAP and WebDataset are not on offer: MCAP is a robotics logging container rather than a training format, and labs shard WebDataset internally. We would rather tell you which is which.
| Format | Status | What consumes it |
|---|---|---|
| LeRobot v2.1 | Standard | Loads in pi0 / openpi and GR00T pipelines. Observations only: retargeting to an action space is your step |
HDF5: EgoDex-style paired .hdf5 + .mp4 | Standard | ALOHA and RoboMimic imitation-learning frameworks |
| FH-Ego v1 JSON | Standard | Our open schema: the full annotation layer, readable without our tooling |
| Zarr v2 | On request | Chunked-array training pipelines |
| TFRecord: RLDS-style | On request | JAX pipelines. Not tfds.load-compatible without a dataset builder, and we say so up front |
| MCAP | Not offered | ROS 2 logging and replay, not policy training |
| WebDataset | Not offered | Labs shard this internally |
Tell us the task, the environment and the policy you are training, and we scope a capture brief against it. The catalogue The catalogue lists capture specifications you can commission, with target volumes and their real availability status.
If you are training a manipulation or imitation-learning policy, the useful first question is not how many hours you need; it is which task, in which environment, on which rig. That is what a brief pins down.