Bimanual manipulation training data

Quick facts

Task

Bimanual manipulation

Modality

dual-arm robot traces or two-hand human demonstrations

Environment

assembly, folding, packing, fixture holding, and tool handoff

Volume

100-500 bimanual task episodes

Format

LeRobot or HDF5 with left/right stream separation

QA

left/right sync, contact handoffs, and role labels for each arm

Comparison

What to specify for bimanual manipulation

The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, LeRobot or HDF5 with left/right stream separation 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

single-arm datasets do not capture coordination, handoffs, or shared-object constraints. 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.

Bimanual manipulation buyer scenario

A realistic bimanual manipulation request starts when a robotics team has a model behavior that fails in assembly, folding, packing, fixture holding, and tool handoff. The team does not just need more video; it needs examples where left/right sync, contact handoffs, and role labels for each arm can be verified repeatedly ^[4].

"We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface."

That means the supplier must show the requested dual-arm robot traces or two-hand human demonstrations, prove the capture context, and deliver LeRobot or HDF5 with left/right stream separation in a way the buyer can test before scaling.

Bimanual manipulation sample acceptance criteria

A useful sample for bimanual 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 100-500 bimanual task episodes ^[6]. If the sample cannot show left/right sync, contact handoffs, and role labels for each arm, the buyer should reject it before funding a larger batch.

Bimanual manipulation task taxonomy and coverage

A bimanual 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. A good sample identifies each arm's role, shows the transfer or coordinated contact, and includes time-sync evidence across views.

Bimanual manipulation accepted and rejected examples

Bimanual data often fails at synchronization. Require camera and arm streams that align through the handoff or coordinated contact moment; otherwise a reviewer cannot tell which arm caused the success or failure. 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.

Bimanual manipulation pilot manifest fields

For Bimanual 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. A good sample identifies each arm's role, shows the transfer or coordinated contact, and includes time-sync evidence across views. 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. Google Research blog

   RT-1 shows real robot data is used to train action-producing manipulation policies.

   robotics-transformer1.github.io ↩
2. Dataset page

   RoboSet includes teleoperated trajectories relevant to coordinated manipulation data.

   robopen.github.io ↩
3. Project site

   UMI-style gripper data supports long-horizon manipulation collection outside narrow lab conditions.

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

   Open X-Embodiment demonstrates why multi-robot data needs consistent observation and action representation.

   robotics-transformer-x.github.io ↩
5. Teleoperation datasets are becoming the highest-intent physical AI content category

   ALOHA presents bimanual demonstrations collected through a custom teleoperation interface.

   tonyzhaozh.github.io ↩
6. Dataset page

   LIBERO distinguishes demonstration styles for manipulation datasets, including teleoperated and kinesthetic data.

   libero-project.github.io ↩
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