Buyer intake

Physical AI data marketplace

Truelabel is a physical AI data marketplace where 100+ vetted capture partners deliver egocentric video, teleoperation traces, robot demonstrations, and evaluation datasets with commercial-use rights, contributor consent artifacts, and per-buyer fitness review. Buyers post a sourcing request, review matched samples, and ingest data with rights and metadata attached.

Updated 2026-05-05

By truelabel

Reviewed by truelabel · May 5, 2026

physical AI data marketplace

Request physical AI data How sourcing works

Quick facts

Request type: NET_NEW exclusive collection
Modality: Egocentric video + hand pose + metadata
Environment: Warehouse picking and packing
Volume: 40-100 hours of accepted footage
Rights: Commercial training license with consent artifacts
Delivery: Buyer S3 or Azure bucket with per-session metadata

Comparison

Option	Best for	Risk
Public datasets	Benchmarks and early experiments	Licensing, coverage, and freshness gaps
Generic annotation vendors	Labeling existing assets	Weak capture supply and robotics context
Internal collection	Strategic proprietary programs	Slow setup, high fixed cost
truelabel sourcing	Niche real-world capture from vetted suppliers	Requires clear spec and sample QA

Provider list — Physical AI data marketplace

14 providers covering physical AI 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.

Why physical AI data is different

Physical AI training data differs from web text in three ways. First, it needs factory-style data pipelines for curation, generation, evaluation, and training ^[1]. Second, physical AI teams need custom robotics data beyond generic labeling programs ^[2]. Third, embodied data must come from agents acting in real environments, with observations and actions preserved for behavior cloning ^[3].

"LeRobotDataset v3.0 is a standardized format for robot learning data. It provides unified access to multi-modal time-series data, sensorimotor signals and multi‑camera video, as well as rich metadata for indexing, search, and visualization on the Hugging Face Hub."
— from LeRobot dataset documentation — Hugging Face

^[4]

The quote underscores why a marketplace has to preserve multi-modal sensorimotor signals and metadata instead of treating physical AI data like commodity web text.

What buyers can post

A request can specify off-the-shelf data, net-new exclusive capture, or a smaller eval set. Buyers specify provenance, capture rig, location, consent, exclusivity, and QA context before suppliers submit samples ^[5]. Episode or trajectory scale belongs in the brief: RoboSet-style teleoperation collections can involve 9.5 thousand accepted teleoperated trajectories, so buyers should define accepted trajectory targets up front ^[6]. Delivery formats should preserve episode steps, observations, actions, rewards, discounts, and metadata ^[7]. Quality bars should be evaluated against task-specific manipulation demonstrations before broad collection begins ^[8].

Egocentric video for robot pretraining and world models
Teleoperation trajectories for policy learning
Manipulation demonstrations for VLA and diffusion-policy work
Eval datasets with consent artifacts and delivery metadata

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

Physical AI data providers: criteria and optionsRelated page Best teleoperation data providers 2026Related page Best VLA training data providers 2026Related page Data provenance for physical AIRelated page What is physical AI training data?Related page Robot training data marketplaceBuyer conversion page Assembly training dataTask-specific requirements Bimanual manipulation training dataTask-specific requirements

External references and source context

NVIDIA: Physical AI Data Factory Blueprint
Physical AI data programs need factory-style pipelines for data curation, generation, evaluation, and training rather than passive web scraping.
investor.nvidia.com ↩
Scale AI: Expanding Our Data Engine for Physical AI
Physical AI teams need custom robotics data beyond generic labeling programs.
scale.com ↩
Project site
Embodied robotics data must come from agents acting in real environments, including robot observations and actions for behavior cloning.
droid-dataset.github.io ↩
LeRobot dataset documentation
LeRobotDataset v3 provides a standardized robot-learning data format for multi-modal time-series data, sensorimotor signals, multi-camera video, and metadata.
Hugging Face ↩
Dataset cards are not yet standardized for physical AI procurement
Commercial physical AI buyers need provenance, capture-rig, location, consent, exclusivity, and QA context beyond a generic dataset card.
Hugging Face ↩
Dataset page
Teleoperation request planning should specify episode or trajectory scale because robotics collections can involve 9.5 thousand accepted teleoperated trajectories.
robopen.github.io ↩
RLDS: Reinforcement Learning Datasets
Episode-based robot-learning datasets need canonical formats that preserve observations, actions, rewards, discounts, and metadata at step level.
GitHub ↩
Dataset page
Quality bars for robot-data sourcing requests should be evaluated against task-specific manipulation demonstrations before broad collection.
libero-project.github.io ↩

FAQ

What is physical AI training data?

Physical AI training data is real-world or simulation-derived data used to train robots, VLA models, world models, and embodied AI systems. It can include egocentric video, teleoperation traces, manipulation demonstrations, pose, IMU, tactile data, metadata, and consent artifacts.

When should a buyer use truelabel instead of a public dataset?

Use truelabel when the model needs commercial rights, a specific environment, fresh capture, consent artifacts, or modalities that public datasets do not provide. Public datasets are useful baselines, but production robotics teams often need data shaped to a deployment context.

What does truelabel verify before delivery?

truelabel keeps sample review, rights constraints, consent artifacts, delivery metadata, and buyer acceptance criteria attached to each sourcing request so the operator and buyer can evaluate whether delivered data matches the original spec.

Can suppliers respond with existing datasets?

Yes. OTS sourcing requests are for existing datasets that a supplier can license quickly. Net-new sourcing requests are collected after contract execution and are typically exclusive to the buyer by default.

Looking for physical AI 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 physical AI data