Task data
Assembly training data
Assembly training data helps physical AI teams collect scoped examples in bench assembly, light manufacturing, repair, and fixture workflows. When sourcing it, specify multi-view video, hand pose, tool use, and task step labels, target volume, delivery format, rights, consent, and QA rules for step order, tool visibility, part state, and failure/recovery examples.
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
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | generic manufacturing videos usually lack step-level annotations and rights clarity |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
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].
[5]"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 from the sample 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.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- Dataset documentation
FurnitureBench demonstration files include observations, actions, rewards, and skill fields for assembly-like tasks.
clvrai.github.io ↩ - Dataset page
LIBERO demonstration datasets support manipulation task evaluation and transfer studies.
libero-project.github.io ↩ - Project site
RoboCasa supplies household manipulation task environments that can frame stepwise assembly checks.
robocasa.ai ↩ - Dataset page
RoboSet teleoperation data illustrates trajectory-level collection for contact-rich robot tasks.
robopen.github.io ↩ - Project site
UMI supports long-horizon manipulation demonstrations relevant to assembly task collection.
umi-gripper.github.io ↩ - Project site
DROID captures real-world manipulation demonstrations that can inform assembly data requests.
droid-dataset.github.io ↩
FAQ
What is robot assembly dataset?
robot assembly dataset refers to data collected for bench assembly, light manufacturing, repair, and fixture workflows. It usually includes multi-view video, hand pose, tool use, and task step labels, metadata, and task outcomes that help train or evaluate physical AI systems.
What should a sourcing request include?
It should include task definition, environment, modality, volume, format, rights, consent, budget, deadline, and QA checks such as step order, tool visibility, part state, and failure/recovery examples.
What format should buyers request?
HDF5, MCAP, MP4 plus structured step manifest is the recommended starting point, but truelabel can route buyer-defined schemas when the training pipeline needs a custom layout.
Can this be exclusive?
Yes. Net-new sourcing requests can request exclusive commercial rights, while off-the-shelf datasets are usually non-exclusive unless the buyer explicitly purchases exclusivity.
Sourcing data for robot assembly dataset
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering robot assembly dataset data with consent artifacts and commercial licensing attached.
Request assembly training data