Task data
Manipulation training data
Manipulation training data helps physical AI teams collect scoped examples in tabletop, shelf, bin, and drawer manipulation. When sourcing it, specify egocentric or wrist video with object and hand pose, target volume, delivery format, rights, consent, and QA rules for hands and manipulated objects in frame for active segments.
Quick facts
- Task
- Manipulation
- Modality
- egocentric or wrist video with object and hand pose
- Environment
- tabletop, shelf, bin, and drawer manipulation
- Volume
- 50-250 accepted manipulation episodes
- Format
- LeRobot, RLDS, HDF5, or buyer-defined schema
- QA
- hands and manipulated objects in frame for active segments
Comparison
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | benchmark datasets often lack the buyer's object set and deployment environment |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
What to specify for manipulation
The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, LeRobot, RLDS, HDF5, or buyer-defined schema 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
benchmark datasets often lack the buyer's object set and deployment environment. 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.
Manipulation buyer scenario
A realistic manipulation request starts when a robotics team has a model behavior that fails in tabletop, shelf, bin, and drawer manipulation. The team does not just need more video; it needs examples where hands and manipulated objects in frame for active segments can be verified repeatedly [4].
[5]"CALVIN is a benchmark and toolkit for language-conditioned robot manipulation skills."
That means the supplier must show the requested egocentric or wrist video with object and hand pose, prove the capture context, and deliver LeRobot, RLDS, HDF5, or buyer-defined schema in a way the buyer can test before scaling.
Manipulation sample acceptance criteria
A useful sample for robot manipulation 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 50-250 accepted manipulation episodes [6]. If the sample cannot show hands and manipulated objects in frame for active segments, 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
- CALVIN GitHub repository
CALVIN is a benchmark and toolkit for language-conditioned robot manipulation skills.
GitHub ↩ - Project site
Meta-World is a simulated manipulation benchmark with many task variants.
meta-world.github.io ↩ - Project site
RLBench supplies language-conditioned robot manipulation benchmark tasks.
sites.google.com ↩ - Project site
Robomimic supports learning from demonstration datasets for robot manipulation policies.
robomimic.github.io ↩ - CALVIN paper
The CALVIN paper describes long-horizon manipulation skill evaluation for embodied agents.
arXiv ↩ - Project site
RoboCasa provides household manipulation task environments for robot learning evaluation.
robocasa.ai ↩
FAQ
What is robot manipulation dataset?
robot manipulation dataset refers to data collected for tabletop, shelf, bin, and drawer manipulation. It usually includes egocentric or wrist video with object and hand pose, 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 hands and manipulated objects in frame for active segments.
What format should buyers request?
LeRobot, RLDS, HDF5, or buyer-defined schema 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 manipulation dataset
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering robot manipulation dataset data with consent artifacts and commercial licensing attached.
Request manipulation training data