Assembly training data

Quick facts

Task

Assembly

Modality

multi-view video, hand pose, tool use, and task step labels

Environment

bench assembly, light manufacturing, repair, and fixture workflows

Volume

100-1,000 assembly attempts across parts and operators

Format

HDF5, MCAP, MP4 plus structured step manifest

QA

step order, tool visibility, part state, and failure/recovery examples

Comparison

What to specify for assembly

The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, HDF5, MCAP, MP4 plus structured step manifest delivery expectations, rights, consent, and what counts as an accepted sample. Registry sources show that task data is only reusable when collection setup and task distribution are explicit ^[1]. Buyers should also pin delivery expectations to formats and documentation they can validate before scale ^[2].

Why public data is usually not enough

generic manufacturing videos usually lack step-level annotations and rights clarity. Benchmark and vendor sources show that task labels, rights, and capture context are not interchangeable across deployments ^[3]. A buyer-specific request lets the team request the exact object set, environment, geography, and QA rubric needed for model training or evaluation.

Assembly buyer scenario

A realistic assembly request starts when a robotics team has a model behavior that fails in bench assembly, light manufacturing, repair, and fixture workflows. The team does not just need more video; it needs examples where step order, tool visibility, part state, and failure/recovery examples can be verified repeatedly ^[4].

"UMI supports long-horizon manipulation demonstrations relevant to assembly task collection."

That means the supplier must show the requested multi-view video, hand pose, tool use, and task step labels, prove the capture context, and deliver HDF5, MCAP, MP4 plus structured step manifest in a way the buyer can test before scaling.

Assembly sample acceptance criteria

A useful sample for robot assembly dataset should include at least one accepted episode, one borderline or failed example, a complete metadata manifest, and a note explaining how the supplier would scale only to 100-1,000 assembly attempts across parts and operators ^[6]. If the sample cannot show step order, tool visibility, part state, and failure/recovery examples, the buyer should reject it before funding a larger batch.

Assembly task taxonomy and coverage

A robot assembly dataset request should be task-specific, not template-level. Define the sub-tasks, capture viewpoints, object/environment coverage, labels, failure modes, and pilot acceptance rules before asking a supplier to scale. A good sample should prove task boundaries, metadata completeness, and reviewable rights before scale.

Assembly accepted and rejected examples

A supplier should provide both accepted and rejected examples. Accepted samples prove the requested fields and task boundaries; rejected samples reveal missing sync, bad labels, unsupported rights, or environment mismatch before scale-up. Use licensing/provenance review for human or workplace footage, and route robotics-ready requests through the robot training data marketplace once the pilot schema is clear.

Assembly pilot manifest fields

For Assembly, the pilot manifest should include task phase, environment, object or route class, camera/sensor keys, action/state fields when applicable, timestamps, outcome label, failure reason, reviewer decision, rights/provenance files, and the target delivery format. Accepted samples should prove task boundaries, metadata completeness, and reviewable rights. The manifest is the bridge between supplier footage and buyer QA: if a reviewer cannot reproduce why a sample passed or failed, the dataset is not ready for scale-up.

Public datasets as references, not drop-in commercial supply

Public robotics datasets can guide schema and benchmark expectations, but commercial use, embodiment fit, action-state coverage, and consent/provenance are dataset-specific. Treat them as references unless official terms and buyer review support the intended use.

Pilot package before scale-up

Require a small loadable pilot with raw media/logs, manifest, labels, accepted and rejected samples, consent/provenance artifacts, and validation in the target format. Reject missing fields, broken sync, unclear boundaries, unsupported rights, and samples with only clean successes. For warehouse, kitchen, or industrial variants, compare the nearest warehouse, kitchen, or industrial sourcing spec before scaling.

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. Dataset documentation

   FurnitureBench demonstration files include observations, actions, rewards, and skill fields for assembly-like tasks.

   clvrai.github.io ↩
2. Dataset page

   LIBERO demonstration datasets support manipulation task evaluation and transfer studies.

   libero-project.github.io ↩
3. Project site

   RoboCasa supplies household manipulation task environments that can frame stepwise assembly checks.

   robocasa.ai ↩
4. Dataset page

   RoboSet teleoperation data illustrates trajectory-level collection for contact-rich robot tasks.

   robopen.github.io ↩
5. Project site

   UMI supports long-horizon manipulation demonstrations relevant to assembly task collection.

   umi-gripper.github.io ↩
6. Project site

   DROID captures real-world manipulation demonstrations that can inform assembly data requests.

   droid-dataset.github.io ↩
7. truelabel egocentric data glossary

   Internal contextual link to the egocentric data definition.

   truelabel.ai
8. truelabel sourcing brief intake

   Internal contextual link to Truelabel's sourcing intake workflow.

   truelabel.ai
9. truelabel VLA training data sourcing

   Internal contextual link to VLA training data sourcing.

   truelabel.ai
10. truelabel warehouse robotics data sourcing

    Internal contextual link to warehouse robotics data sourcing.

    truelabel.ai
11. truelabel kitchen manipulation data sourcing

    Internal contextual link to kitchen manipulation data sourcing.

    truelabel.ai
12. truelabel LeRobot format guide

    Internal contextual link to the LeRobot format guide.

    truelabel.ai
13. truelabel LeRobot dataset alternative comparison

    Internal contextual link to the LeRobot dataset alternative comparison.

    truelabel.ai
14. truelabel eval data for robotics hub

    Internal contextual link to robotics eval data sourcing.

    truelabel.ai
15. truelabel teleoperation training-data page

    Internal contextual link to teleoperation training data sourcing.

    truelabel.ai
16. truelabel robot demonstrations training-data page

    Internal contextual link to robot demonstration training data sourcing.

    truelabel.ai
17. truelabel hand-object interaction data page

    Internal contextual link to hand-object interaction training data requirements.

    truelabel.ai
18. truelabel egocentric video datasets hub

    Internal contextual link to the egocentric video datasets hub.

    truelabel.ai

FAQ