Teleoperation training data

Quick facts

Task

Teleoperation

Modality

robot state, action traces, and synchronized camera streams

Environment

robot workcells, warehouses, kitchens, and labs

Volume

a buyer-defined teleoperation volume

Format

MCAP, ROS bag, HDF5, RLDS, or LeRobot

QA

timestamp alignment, state/action completeness, and recoverable failure examples

Comparison

What to specify for teleoperation

The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, MCAP, ROS bag, HDF5, RLDS, or LeRobot 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

video-only demonstrations cannot train action-producing policies without robot state. 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.

Teleoperation buyer scenario

A realistic teleoperation request starts when a robotics team has a model behavior that fails in robot workcells, warehouses, kitchens, and labs. The team does not just need more video; it needs examples where timestamp alignment, state/action completeness, and recoverable failure examples can be verified repeatedly ^[4].

"RoboSet explicitly documents teleoperated trajectories for robot manipulation datasets."

That means the supplier must show the requested robot state, action traces, and synchronized camera streams, prove the capture context, and deliver MCAP, ROS bag, HDF5, RLDS, or LeRobot in a way the buyer can test before scaling.

Teleoperation sample acceptance criteria

A useful sample for teleoperation 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 a buyer-defined teleoperation volume ^[6]. If the sample cannot show timestamp alignment, state/action completeness, and recoverable failure examples, the buyer should reject it before funding a larger batch.

Teleoperation task taxonomy and coverage

A teleoperation 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 include synchronized observations, actions, robot state, controller notes, and a loadable episode manifest.

Teleoperation accepted and rejected examples

Teleoperation data fails when it is just robot video. Require controller provenance, action/state schema, timestamps, and recovery labels so the buyer can distinguish policy-training traces from passive demonstrations. 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.

Teleoperation pilot manifest fields

For Teleoperation, 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 include synchronized observations, actions, robot state, controller notes, and a loadable episode manifest. 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. Teleoperation datasets are becoming the highest-intent physical AI content category

   ALOHA uses a custom teleoperation interface to collect real demonstrations.

   tonyzhaozh.github.io ↩
2. Google Research blog

   RT-1 is a real robot action-learning reference that depends on observation-action data.

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

   UMI is a portable gripper data collection project for in-the-wild manipulation demonstrations.

   umi-gripper.github.io ↩
4. Project site

   Open X-Embodiment normalizes robot observations and actions across embodiments for policy training.

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

   RoboSet explicitly documents teleoperated trajectories for robot manipulation datasets.

   robopen.github.io ↩
6. LeRobot GitHub repository

   LeRobot provides tooling for recording, converting, and training with robot 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