Robot demonstrations training data

Quick facts

Task

Robot demonstrations

Modality

video plus task outcome labels

Environment

home, warehouse, and workshop tasks

Volume

a buyer-defined episode count

Format

MP4 plus JSON or HDF5 metadata

QA

complete task boundaries and visible object interactions

Comparison

What to specify for robot demonstrations

The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, MP4 plus JSON or HDF5 metadata 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

public videos rarely include rights, task labels, or acceptance metadata. 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.

Robot demonstrations buyer scenario

A realistic robot demonstrations request starts when a robotics team has a model behavior that fails in home, warehouse, and workshop tasks. The team does not just need more video; it needs examples where complete task boundaries and visible object interactions can be verified repeatedly ^[4].

"RoboTurk provides real robot demonstration data suitable for imitation learning research."

That means the supplier must show the requested video plus task outcome labels, prove the capture context, and deliver MP4 plus JSON or HDF5 metadata in a way the buyer can test before scaling.

Robot demonstrations sample acceptance criteria

A useful sample for robot demonstration data 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 a buyer-defined episode count ^[6]. If the sample cannot show complete task boundaries and visible object interactions, the buyer should reject it before funding a larger batch.

Robot demonstrations task taxonomy and coverage

A robot demonstration data 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 sample loads as episodes, states the demonstrator mode, preserves task instruction and outcome, and attaches rights/provenance files.

Robot demonstrations accepted and rejected examples

Robot-demonstration pages fail when a supplier sends success-only videos without action/state, embodiment, or episode boundary metadata. Require at least one accepted success, one rejected boundary example, and one failure or recovery episode so reviewers can see what the policy should not learn. 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.

Robot demonstrations pilot manifest fields

For Robot demonstrations, 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 sample loads as episodes, states the demonstrator mode, preserves task instruction and outcome, and attaches rights/provenance files. 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

   DROID provides real-world robot demonstration data for manipulation policy learning.

   droid-dataset.github.io ↩
2. Project site

   Open X-Embodiment frames diverse robot demonstrations as reusable policy-training data.

   robotics-transformer-x.github.io ↩
3. Dataset page

   RoboSet separates teleoperated and demonstration trajectories for robot learning datasets.

   robopen.github.io ↩
4. LeRobot documentation

   LeRobot documentation is a developer entry point for recording and using robot learning datasets.

   Hugging Face ↩
5. Real robot dataset

   RoboTurk provides real robot demonstration data suitable for imitation learning research.

   roboturk.stanford.edu ↩
6. RLDS: Reinforcement Learning Datasets

   RLDS defines episode and step structure for sequential 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