Solutions

Data for the robot, and a verdict once it is working

Two products. One captures and structures the manipulation data a humanoid learns from. The other tells you how that humanoid is actually performing once it is on the line.

Humanoid Training Data

Task-directed manipulation data, captured or structured

We capture task-directed egocentric data of real operators inside our contracted factory network, on our kits or on hardware you supply. If you already hold operator footage, send it instead. Either way you receive structured, labelled, training-ready datasets.

Sourced collection across a large contracted factory network
Or structuring of footage you already hold
Temporal action segments, labels, masks, contact events and trajectories
Delivered in HDF5, LeRobot or RLDS, or a schema you define

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Deployment Evaluation

How the robot is really doing on the line

Once your humanoid is working a station, we compare its cycles against the human baseline drawn from the same operator data we already structure. You see which step is slow, which step failed, and what to fix next.

Scored against a human baseline, step by step
Slow and failed steps surfaced before they become downtime
Edge cases captured on the line feed back into training data
Works from standard video, no extra sensors on the station

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Running a plant rather than a robot programme? Our process intelligence platform for manufacturers is a separate, commercially deployed product. See the platform at khenda.com(opens in a new tab)

How this data compares

The usual ways teams source manipulation data, set against data captured inside a working production plant.

How this data compares	Khenda	Teleoperation	Simulation	Manual labelling
Data source	Real operators doing real work in production plants	Remote operators driving a rig	Synthetic environments	Annotators working over existing video
Getting started	Send footage you already hold, or define a task brief for capture	Build the rig, recruit and train operators	Author scenes, assets and physics	Recruit annotators, write and maintain guidelines
Consent and provenance	Explicit individual operator consent, signed employer agreements, documentation with every dataset	Operator agreements, varies by provider	Not applicable, no human subjects	Inherited from whoever recorded the source footage
Annotation depth	Action boundaries, labels, masks, contact events and trajectories on every segment	Whatever the rig logs	Ground truth by construction, for a world you built	As deep as the budget and the guidelines allow
Real-world fidelity	Native. Takt pressure, clutter and part variation are in the data	Partial. Real contact, staged context	Low. No contact realism	Only as good as the source footage
Scale	A multi-plant network, plus any archive you already hold	Bounded by operators and rigs	Bounded by compute and authoring effort	Bounded by annotator hours

Start with one task

Tell us the skill you are training, or the footage you already hold. We will tell you what we can deliver and in what format.

Talk to our team