Dexterous manipulation training data

Quick facts

Task

Dexterous manipulation

Modality

egocentric video, hand pose, tactile or glove signals

Environment

tools, small objects, drawers, fasteners, and deformables

Volume

the buyer-approved target volume

Format

MCAP, HDF5, LeRobot, or synchronized video plus pose tracks

QA

finger visibility, contact phases, and precise task segmentation

Comparison

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].

"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 only to the buyer-approved target volume ^[6]. If the sample cannot show finger visibility, contact phases, and precise task segmentation, the buyer should reject it before funding a larger batch.

Dexterous manipulation task taxonomy and coverage

A dexterous manipulation 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. Accepted samples show fingertip visibility or a documented proxy, object orientation changes, and failure modes such as slip or misalignment.

Dexterous manipulation accepted and rejected examples

Dexterous pages should reject generic manipulation footage. If fingertips, in-hand object state, and small corrections are not visible or labeled, the data is unlikely to support dexterous policy learning. 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.

Dexterous manipulation pilot manifest fields

For Dexterous manipulation, 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 show fingertip visibility or a documented proxy, object orientation changes, and failure modes such as slip or misalignment. 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. Project site

   BC-Z contributes multi-view visual observations for manipulation policy learning.

   sites.google.com ↩
2. NVIDIA GR00T N1 technical report

   GR00T N1 frames humanoid manipulation data as multimodal robot training material.

   arXiv ↩
3. Google Research blog

   RT-1 is a real-robot manipulation data reference for action-producing policies.

   robotics-transformer1.github.io ↩
4. Project site

   Robosuite provides manipulation environments for contact-rich policy evaluation.

   robosuite.ai ↩
5. HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction

   HOI4D provides dexterous hand-object interaction evidence for object manipulation tasks.

   hoi4d.github.io ↩
6. LeRobot GitHub repository

   LeRobot tooling can represent synchronized observations and actions for robot learning datasets.

   GitHub ↩
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