Task data
Dexterous manipulation training data
Dexterous manipulation training data helps physical AI teams collect scoped examples in tools, small objects, drawers, fasteners, and deformables. When sourcing it, specify egocentric video, hand pose, tactile or glove signals, target volume, delivery format, rights, consent, and QA rules for finger visibility, contact phases, and precise task segmentation.
Quick facts
- Task
- Dexterous manipulation
- Modality
- egocentric video, hand pose, tactile or glove signals
- Environment
- tools, small objects, drawers, fasteners, and deformables
- Volume
- 20-80 hours of high-precision manipulation footage
- Format
- MCAP, HDF5, LeRobot, or synchronized video plus pose tracks
- QA
- finger visibility, contact phases, and precise task segmentation
Comparison
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | general egocentric datasets rarely include finger-level metadata or tactile context |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
What to specify for dexterous manipulation
The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, MCAP, HDF5, LeRobot, or synchronized video plus pose tracks 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
general egocentric datasets rarely include finger-level metadata or tactile context. 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.
Dexterous manipulation buyer scenario
A realistic dexterous manipulation request starts when a robotics team has a model behavior that fails in tools, small objects, drawers, fasteners, and deformables. The team does not just need more video; it needs examples where finger visibility, contact phases, and precise task segmentation can be verified repeatedly [4].
[5]"HOI4D provides dexterous hand-object interaction evidence for object manipulation tasks."
That means the supplier must show the requested egocentric video, hand pose, tactile or glove signals, prove the capture context, and deliver MCAP, HDF5, LeRobot, or synchronized video plus pose tracks in a way the buyer can test before scaling.
Dexterous manipulation sample acceptance criteria
A useful sample for dexterous 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 20-80 hours of high-precision manipulation footage [6]. If the sample cannot show finger visibility, contact phases, and precise task segmentation, 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
- Project site
BC-Z contributes multi-view visual observations for manipulation policy learning.
sites.google.com ↩ - NVIDIA GR00T N1 technical report
GR00T N1 frames humanoid manipulation data as multimodal robot training material.
arXiv ↩ - Google Research blog
RT-1 is a real-robot manipulation data reference for action-producing policies.
robotics-transformer1.github.io ↩ - Project site
Robosuite provides manipulation environments for contact-rich policy evaluation.
robosuite.ai ↩ - Project site
HOI4D provides dexterous hand-object interaction evidence for object manipulation tasks.
hoi4d.github.io ↩ - LeRobot GitHub repository
LeRobot tooling can represent synchronized observations and actions for robot learning datasets.
GitHub ↩
FAQ
What is dexterous manipulation dataset?
dexterous manipulation dataset refers to data collected for tools, small objects, drawers, fasteners, and deformables. It usually includes egocentric video, hand pose, tactile or glove signals, 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 finger visibility, contact phases, and precise task segmentation.
What format should buyers request?
MCAP, HDF5, LeRobot, or synchronized video plus pose tracks 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 dexterous manipulation dataset
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering dexterous manipulation dataset data with consent artifacts and commercial licensing attached.
Request dexterous manipulation training data