Task data
Robot demonstrations training data
Robot demonstrations training data helps physical AI teams collect scoped examples in home, warehouse, and workshop tasks. When sourcing it, specify video plus task outcome labels, target volume, delivery format, rights, consent, and QA rules for complete task boundaries and visible object interactions.
Quick facts
- Task
- Robot demonstrations
- Modality
- video plus task outcome labels
- Environment
- home, warehouse, and workshop tasks
- Volume
- 25-100 accepted task episodes
- Format
- MP4 plus JSON or HDF5 metadata
- QA
- complete task boundaries and visible object interactions
Comparison
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | public videos rarely include rights, task labels, or acceptance metadata |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
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].
[5]"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 from the sample to 25-100 accepted task episodes [6]. If the sample cannot show complete task boundaries and visible object interactions, 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
DROID provides real-world robot demonstration data for manipulation policy learning.
droid-dataset.github.io ↩ - Project site
Open X-Embodiment frames diverse robot demonstrations as reusable policy-training data.
robotics-transformer-x.github.io ↩ - Dataset page
RoboSet separates teleoperated and demonstration trajectories for robot learning datasets.
robopen.github.io ↩ - LeRobot documentation
LeRobot documentation is a developer entry point for recording and using robot learning datasets.
Hugging Face ↩ - Real robot dataset
RoboTurk provides real robot demonstration data suitable for imitation learning research.
roboturk.stanford.edu ↩ - RLDS: Reinforcement Learning Datasets
RLDS defines episode and step structure for sequential robot-learning datasets.
GitHub ↩
FAQ
What is robot demonstration data?
robot demonstration data refers to data collected for home, warehouse, and workshop tasks. It usually includes video plus task outcome labels, 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 complete task boundaries and visible object interactions.
What format should buyers request?
MP4 plus JSON or HDF5 metadata 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 demonstration data
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering robot demonstration data data with consent artifacts and commercial licensing attached.
Request robot demonstrations training data