Task data
Bimanual manipulation training data
Bimanual manipulation training data helps physical AI teams collect scoped examples in assembly, folding, packing, fixture holding, and tool handoff. When sourcing it, specify dual-arm robot traces or two-hand human demonstrations, target volume, delivery format, rights, consent, and QA rules for left/right sync, contact handoffs, and role labels for each arm.
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
| Source | Use | Limitation |
|---|---|---|
| Public dataset | Research baseline | single-arm datasets do not capture coordination, handoffs, or shared-object constraints |
| Internal capture | Maximum control | Slow setup and high fixed cost |
| truelabel sourcing | Spec-matched supplier response | Requires clear acceptance criteria |
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].
[5]"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 from the sample 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.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- Google Research blog
RT-1 shows real robot data is used to train action-producing manipulation policies.
robotics-transformer1.github.io ↩ - Dataset page
RoboSet includes teleoperated trajectories relevant to coordinated manipulation data.
robopen.github.io ↩ - Project site
UMI-style gripper data supports long-horizon manipulation collection outside narrow lab conditions.
umi-gripper.github.io ↩ - Project site
Open X-Embodiment demonstrates why multi-robot data needs consistent observation and action representation.
robotics-transformer-x.github.io ↩ - Teleoperation datasets are becoming the highest-intent physical AI content category
ALOHA presents bimanual demonstrations collected through a custom teleoperation interface.
tonyzhaozh.github.io ↩ - Dataset page
LIBERO distinguishes demonstration styles for manipulation datasets, including teleoperated and kinesthetic data.
libero-project.github.io ↩
FAQ
What is bimanual manipulation dataset?
bimanual manipulation dataset refers to data collected for assembly, folding, packing, fixture holding, and tool handoff. It usually includes dual-arm robot traces or two-hand human demonstrations, 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 left/right sync, contact handoffs, and role labels for each arm.
What format should buyers request?
LeRobot or HDF5 with left/right stream separation 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 bimanual manipulation dataset
Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering bimanual manipulation dataset data with consent artifacts and commercial licensing attached.
Request bimanual manipulation training data