Robotics datasets
Robot training data marketplace
A robot training data marketplace coordinates demonstrations, trajectories, video, robot state, action streams, and evaluation sets across vetted capture partners. Truelabel converts buyer requirements into supplier-facing specs, routes them to capture partners with the right embodiment and rights posture, and runs sample review before scale.
Quick facts
- Task
- Manipulation, grasping, sorting, navigation, or assembly
- Environment
- Warehouse, kitchen, workshop, factory, office, or outdoor
- Modality
- Video, robot states, actions, pose, IMU, tactile, metadata
- License
- Commercial model-training rights
- Acceptance
- Sample QA and delivery acceptance before payout
Comparison
| Approach | Works when | Watch out for |
|---|---|---|
| Academic dataset | You need a baseline benchmark | License and task fit may be wrong |
| Internal lab | You own rigs and operators | Slow scaling across environments |
| Data vendor | The scope is standard | May not have niche capture supply |
| truelabel | The spec is niche and supplier fit matters | Requires clear sourcing requirements |
Provider list — Robot training data marketplace
14 providers covering robot training data marketplace. Each entry summarizes the provider's strongest fit and a buyer-bottleneck signal so you can shortcut the discovery loop.
#1
Scale AI
Enterprise data engine for autonomous-vehicle and robotics labeling, with managed annotation operations and large-scale data factories.
Best for: Enterprise programs that need one end-to-end vendor for labeling and curation, but expect long sales cycles and limited self-service.
#2
Encord
Annotation platform with active-learning workflows and an API-first labeling stack for ML teams.
Best for: Teams that want to own labeling tooling and integrate review loops into their model pipeline.
#3
Appen
Crowdsourced labeling and capture network for speech, vision, and structured data, with a long-running training-data marketplace.
Best for: High-volume annotation where contributor diversity matters more than robotics-specific physical capture.
#4
Kognic
Annotation and curation specialist focused on automotive perception with multi-sensor sync.
Best for: Sensor-fusion datasets where camera/lidar/radar timing alignment is the bottleneck.
#5
Segments.ai
Self-serve labeling platform with strong 3D point-cloud and segmentation tooling.
Best for: Engineering teams shipping point-cloud or 3D-instance labels at moderate scale.
#6
V7 Darwin
Annotation tool focused on medical and computer-vision domains with workflow automation.
Best for: CV labeling outside robotics when image annotation throughput is the bottleneck.
#7
NVIDIA Cosmos / Isaac Sim
Synthetic data generation and simulation stack from NVIDIA, covering Cosmos predictive world model and Isaac Sim/Lab for robot training.
Best for: Sim-first programs that need high-volume cheap data with photoreal generation and scriptable scenes.
#8
Hugging Face robotics datasets
Open-access aggregator of community-contributed robotics datasets — LeRobot, Open X-Embodiment slices, DROID, BridgeData V2, and 1,000+ records.
Best for: Discovery and benchmark research; not procurement-ready without per-dataset rights and consent review.
#9
Open X-Embodiment
22-dataset cross-embodiment robotics corpus from 21 institutions — the closest thing to ImageNet for manipulation.
Best for: Pretraining cross-embodiment policies before deployment-specific fine-tune.
#10
DROID
76k real-world robot demonstrations across 564 scenes from 13 institutions, primarily single-arm Franka data.
Best for: Real-world manipulation pretraining when your target robot is single-arm Franka or close cousins.
#11
BridgeData V2
60,096 trajectories across 24 environments — a workhorse benchmark for behavior cloning research.
Best for: Imitation-learning baselines on tabletop manipulation tasks.
#12
Mobile ALOHA
Open-hardware bimanual mobile-manipulation platform with public demonstration datasets from Stanford.
Best for: Bimanual mobile-manipulation research where you can replicate the hardware platform.
#13
RoboCat training data
DeepMind's self-improving generalist robotic manipulation agent — research reference for cross-embodiment learning.
Best for: Reference architecture for self-improving training loops; underlying data is not publicly redistributable.
#14
Figure × Brookfield
Industrial humanoid partnership giving Figure access to Brookfield real-estate properties for capture and field training.
Best for: Reference for industrial-scale field data partnerships; not directly purchasable as a dataset.
What a robotics data sourcing request should contain
A robotics sourcing request should describe the task, robot or human demonstrator, object set, environment, geography, capture hardware, episode length, metadata, rights, consent, budget, deadline, and what counts as accepted delivery [1]. Buyers should also specify the sample format before supplier selection because episode structure [2], time-synchronized streams [3], and structured arrays [4] decide whether a small proof package can scale into training-ready data.
[5]"LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch."
That quote is the practical bar for marketplace intake: the request has to ask for data that can move from supplier sample to robotics tooling without a hidden conversion project.
Why sample review comes first
Small samples expose the problems that make robot datasets unusable: bad synchronization, missing metadata, repetitive scenes, incomplete tasks, unclear consent, or format drift from the buyer's training pipeline [6]. Real-world robot datasets such as DROID show why scene, task, and embodiment fit should be inspected before scaling collection [7]. Multi-embodiment releases such as Robotics Transformer X reinforce the same rule: sample review should verify provenance, task diversity, and delivery schema before a buyer funds volume [8].
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- NVIDIA: Physical AI Data Factory Blueprint
Robot training data programs need curation, evaluation, and training workflows before they are useful at scale.
investor.nvidia.com ↩ - RLDS: Reinforcement Learning Datasets
Episode-level dataset structure matters for robot training data because reinforcement learning datasets carry observations, actions, rewards, and metadata across time.
GitHub ↩ - MCAP file format
Robotics sourcing requests should define delivery formats that preserve time-synchronized streams and schema information before supplier samples are accepted.
mcap.dev ↩ - HDF5 format overview
Dense robot trajectories and arrays need structured container formats so samples can be checked before full delivery.
hdfgroup.org ↩ - LeRobot GitHub repository
Robot training data marketplaces should ask for sample files in a tool-compatible robotics dataset format before approving larger collection.
GitHub ↩ - scale.com scale ai universal robots physical ai
Commercial robotics teams need custom physical AI data beyond generic annotation when the capture scope is specific.
scale.com ↩ - Project site
Useful robot training data samples should prove task, scene, embodiment, and metadata fit before the buyer scales a collection.
droid-dataset.github.io ↩ - Project site
Multi-embodiment robot training data benefits from explicit dataset provenance, task diversity, and sample review before procurement expands.
robotics-transformer-x.github.io ↩
FAQ
What counts as robot training data?
Robot training data can include video, states, actions, trajectories, demonstrations, pose tracks, tactile readings, metadata, and outcome labels. The data should map clearly to the model or evaluation task.
Can truelabel help with custom robot datasets?
truelabel is built for custom request intake. Buyers can define the modality, environment, task, rights, volume, and delivery format, then review supplier samples before scaling the collection.
Are public robotics datasets enough?
Public datasets are useful for research and baselines, but production teams often need commercial rights, new environments, specific tasks, and current capture conditions that public datasets do not provide.
Who supplies the data?
Suppliers are vetted capture partners, teleoperation providers, mocap shops, and data collection teams that can submit samples matching the buyer's request requirements.
Looking for robot training data marketplace?
Specify modality, task, environment, rights, and delivery format. Truelabel matches you with vetted capture partners — every delivery includes consent artifacts and commercial licensing by default.
Request robot training data