Task data
Grasping training data
Grasping training data helps physical AI teams collect scoped examples in bins, shelves, tabletops, and cluttered work surfaces. When sourcing it, specify multi-view video, object labels, and grasp outcome metadata, target volume, delivery format, rights, consent, and QA rules for outcome labels, object visibility, and repeated attempts across object shapes.
Quick facts
- Task
- Grasping
- Modality
- multi-view video, object labels, and grasp outcome metadata
- Environment
- bins, shelves, tabletops, and cluttered work surfaces
- Volume
- 500-2,000 grasp attempts with success and failure labels
- Format
- HDF5, JSONL, MCAP, or image/video folders with manifest
- QA
- outcome labels, object visibility, and repeated attempts across object shapes
Comparison
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | synthetic grasp datasets can miss lighting, clutter, and real contact dynamics |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
What to specify for grasping
The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, HDF5, JSONL, MCAP, or image/video folders with 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
synthetic grasp datasets can miss lighting, clutter, and real contact dynamics. 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.
Grasping buyer scenario
A realistic grasping request starts when a robotics team has a model behavior that fails in bins, shelves, tabletops, and cluttered work surfaces. The team does not just need more video; it needs examples where outcome labels, object visibility, and repeated attempts across object shapes can be verified repeatedly [4].
[5]"HOI4D covers hand-object interaction data relevant to grasping and object manipulation."
That means the supplier must show the requested multi-view video, object labels, and grasp outcome metadata, prove the capture context, and deliver HDF5, JSONL, MCAP, or image/video folders with manifest in a way the buyer can test before scaling.
Grasping sample acceptance criteria
A useful sample for grasping 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 500-2,000 grasp attempts with success and failure labels [6]. If the sample cannot show outcome labels, object visibility, and repeated attempts across object shapes, the buyer should reject it before funding a larger batch.
Grasping task taxonomy and coverage
A grasping 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 pilot includes approach frames, contact labels, object category/material, success/failure outcome, and rejected clips showing why grasps failed.
| Planning area | Specify | QA question |
|---|---|---|
| Task phases | start/end boundaries, success, failure, recovery | Can reviewers identify every phase? |
| Sensors | wrist/external/egocentric video, state/action, depth/tactile if needed | Are streams synchronized and loadable? |
| Objects and environment | object family, layout, lighting, clutter, material | Does coverage match deployment? |
| Rights and provenance | license, consent, site permission, source manifest | Can legal/procurement audit the source? |
| Grasp approach | pre-shape, approach vector, target face | camera misses the pre-grasp |
| Contact outcome | stable, slip, drop, crush, no-grasp | binary success hides failures |
| Object coverage | rigid, deformable, reflective, cluttered | only easy rigid objects appear |
Grasping accepted and rejected examples
Grasping datasets need negative outcomes. A rights-cleared pile of successful pick clips is less useful than a smaller package that labels slips, drops, partial grasps, occlusions, and object classes that are difficult for the target gripper. 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.
Grasping pilot manifest fields
For Grasping, 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. A good pilot includes approach frames, contact labels, object category/material, success/failure outcome, and rejected clips showing why grasps failed. 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
- Project site
DexYCB is a hand-object dataset centered on YCB object interactions.
dex-ycb.github.io ↩ - Project site
BC-Z contributes multi-view manipulation data that informs grasping and object handling tasks.
sites.google.com ↩ - Project site
Robosuite provides simulation environments and manipulation tasks including object interaction settings.
robosuite.ai ↩ - NVIDIA GR00T N1 technical report
GR00T N1 technical material illustrates physical AI training requirements for humanoid manipulation tasks.
arXiv ↩ - HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
HOI4D covers hand-object interaction data relevant to grasping and object manipulation.
hoi4d.github.io ↩ - LeRobot GitHub repository
LeRobot provides tooling for robot learning datasets that can carry grasp attempts and outcomes.
GitHub ↩ - truelabel egocentric data glossary
Internal contextual link to the egocentric data definition.
truelabel.ai - truelabel sourcing brief intake
Internal contextual link to Truelabel's sourcing intake workflow.
truelabel.ai - truelabel VLA training data sourcing
Internal contextual link to VLA training data sourcing.
truelabel.ai - truelabel warehouse robotics data sourcing
Internal contextual link to warehouse robotics data sourcing.
truelabel.ai - truelabel kitchen manipulation data sourcing
Internal contextual link to kitchen manipulation data sourcing.
truelabel.ai - truelabel LeRobot format guide
Internal contextual link to the LeRobot format guide.
truelabel.ai - truelabel LeRobot dataset alternative comparison
Internal contextual link to the LeRobot dataset alternative comparison.
truelabel.ai - truelabel eval data for robotics hub
Internal contextual link to robotics eval data sourcing.
truelabel.ai - truelabel teleoperation training-data page
Internal contextual link to teleoperation training data sourcing.
truelabel.ai - truelabel robot demonstrations training-data page
Internal contextual link to robot demonstration training data sourcing.
truelabel.ai - truelabel hand-object interaction data page
Internal contextual link to hand-object interaction training data requirements.
truelabel.ai - truelabel egocentric video datasets hub
Internal contextual link to the egocentric video datasets hub.
truelabel.ai
FAQ
What is grasping dataset?
grasping dataset refers to data collected for bins, shelves, tabletops, and cluttered work surfaces. It usually includes multi-view video, object labels, and grasp outcome metadata, 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 outcome labels, object visibility, and repeated attempts across object shapes.
What format should buyers request?
HDF5, JSONL, MCAP, or image/video folders with 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.
What should a grasping dataset request include?
Include target task phases, environment and object coverage, sensors/cameras, action/state fields when applicable, labels, success/failure outcomes, privacy/licensing artifacts, delivery format, and pilot acceptance criteria.
Sourcing data for grasping dataset
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering grasping dataset data with consent artifacts and commercial licensing attached.
Request grasping training data