Teleoperation
Trained operators drive your robot through the task. Every attempt is recorded.
Session, operator, and task recordAuraOne / Human Data / Physical AI
Loading Physical AI.
Loading capture profiles, review, and delivery details.
AuraOne / Human Data / Physical AI
Loading capture profiles, review, and delivery details.
AuraOne / Human Data / Physical AI
Trained operators record the demonstrations your robot needs. You get reviewed episodes, not raw footage.
Robots cannot learn a task from the internet. Somebody has to do it first, on camera, correctly, thousands of times.
What we capture
A video of a hand opening a drawer teaches a model very little. The trajectory, the force, and the moment it slips teach it a lot.
Trained operators drive your robot through the task. Every attempt is recorded.
Session, operator, and task recordPeople perform the task themselves while cameras and wearables capture how.
Video, pose, and device metadataVideo, trajectory, and force streams recorded in sync.
Aligned streams with timestampsReviewers watch every episode. Bad ones get reworked or thrown out.
Disposition, reason, and rework historyDropped frames, clock drift, and sensor faults get caught before delivery.
Validation report per episodeDatasets ship with manifests, checksums, and a data card. Every version is reproducible.
Manifest, checksums, and data cardName the skill. We find the operators, run the capture, and review every episode before it reaches you.
Input
Work
Output
Four steps. Each one has an owner and a record you can inspect afterward.
01
Name the skill, the environment, and what counts as a pass.
Approved task brief
02
Rights are attached before the first frame. Operators see what they signed.
Consent receipt and license scope
03
Operators record on approved profiles. Every stream ties back to its task and device.
Session, device, and stream metadata
04
Reviewers watch every episode. Failures get labeled, not deleted.
Disposition, reason, and failure labels
05
Accepted episodes ship with manifests, checksums, and a data card.
Delivery manifest and acknowledgment
What you receive
Your contract sets the exact scope. Managed robotics programs collect and review human demonstrations, then coordinate a monitored handoff; queued transcode, copy, or webhook work is not represented as delivered.
What you receive
Raw is never overwritten. Clean removes the dead time. Intelligence explains what happened.
The unchanged evidence, exactly as it was collected.
The accepted task content, with the dead time removed.
The record that explains what happened, and whether it worked.
Capture profiles
A phone scales to volume. Wearables catch the hands. Both carry their full device and kit lineage.
First-person capture from an approved phone configuration. Scales to volume.
Multi-view capture for tasks where hand visibility and timing matter.
Where we capture
A kitchen at 7am does not look like a kitchen in a dataset. We record in the places your robot will actually work.
Each outcome depends on the scope your program agreed to.
| Outcome | Work | What you receive | Program fit |
|---|---|---|---|
| Train a manipulation policy | Collect successful demonstrations of one skill across varied objects and settings. | Episode count, task coverage, and accept rate. | Coverage applies to the environments and objects in the brief. |
| Build a failure set | Keep the attempts that went wrong and label how they went wrong. | Failure labels, source episodes, and reviewer rationale. | Reuse depends on your rights scope and retention policy. |
| Benchmark a robot release | Replay a fixed episode set against a new policy and compare outcomes. | Held-out episodes, scoring criteria, and per-episode results. | Results apply to the scoped episode set and criteria version. |
| Expand into a new environment | Run the same skill in homes, warehouses, or retail floors. | Environment coverage, location consent, and privacy zones. | Each site requires its own consent and privacy review. |