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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. US search demand for 'physical AI' grew 3.5× between May 2025 and April 2026 (1,900 → 6,600 monthly searches), driven by humanoid programs (NVIDIA GR00T N1, Figure AI commercial deployments), open-source policy releases (Open X-Embodiment, RoboCasa), and the maturation of factory-style data pipelines that replace passive web scraping. Buyers post a sourcing request, review matched samples, and ingest data with rights and metadata attached.

Updated 2026-05-21
By TrueLabel Sourcing
Reviewed by TrueLabel Sourcing ·
physical AI data marketplace
Request physical AI dataHow sourcing works
3.5×US search demand growth for 'physical AI' (12mo)
100+Vetted capture partners across embodiments
6,600Monthly US searches for 'physical AI' (Apr 2026)

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

OptionBest forRisk
Public datasetsBenchmarks and early experimentsLicensing, coverage, and freshness gaps
Generic annotation vendorsLabeling existing assetsWeak capture supply and robotics context
Internal collectionStrategic proprietary programsSlow setup, high fixed cost
truelabel sourcingNiche real-world capture from vetted suppliersRequires 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. #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. #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. #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. #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. #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. #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. #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. #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. #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. #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. #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. #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. #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. #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.

The 2026 physical AI demand surge

Search interest for 'physical AI' grew from 1,900 monthly US searches in May 2025 to 6,600 by April 2026 — a 3.5× expansion driven by three forcing functions [1]. First, humanoid foundation models like NVIDIA GR00T N1 require heterogeneous data pyramids (teleoperation, egocentric video, synthetic generation) that no single vendor can supply [2]. Second, commercial humanoid deployments — Figure AI in Brookfield logistics facilities is the lead indicator — convert exploratory demand into sustained procurement for thousands of hours of whole-body teleoperation and warehouse manipulation data [3]. Third, Open X-Embodiment exposed the heterogeneity gap: 527 skills across 22 robot embodiments still leaves proprietary buyers needing exclusive capture for the embodiment and task distribution they actually deploy [4].

The buy-side response is moving from one-off vendor engagements to factory-style pipelines for curation, generation, evaluation, and training [1]. That shape — pipeline, not procurement — is what a marketplace serves.

  • Humanoid programs (Figure, 1X, Agility, Apptronik) — whole-body teleoperation at scale
  • Warehouse + commercial logistics — bin-picking, packing, multi-robot coordination
  • Household + domestic tasks — laundry, kitchen, cleaning, kid-care egocentric capture
  • Retail + last-mile — shelf-stocking, fulfillment, delivery-handoff demonstrations

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 [5]. Third, embodied data must come from agents acting in real environments, with observations and actions preserved for behavior cloning [6].

"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."

[7]

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 [8]. 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 [9]. Delivery formats should preserve episode steps, observations, actions, rewards, discounts, and metadata [10]. Quality bars should be evaluated against task-specific manipulation demonstrations before broad collection begins [11].

  • 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

Sub-vertical sourcing playbooks

Buyer demand splits into five durable sub-verticals, each with distinct embodiment, environment, and licensing requirements. Warehouse and commercial logistics — bin-picking, packing, multi-robot coordination — drive the largest volume of teleoperation traces today, anchored by humanoid logistics deployments [3]; the matching sourcing-spec for capture is /sourcing/teleop-warehouse. Household and domestic tasks (laundry, kitchen, cleaning, kid-care) require egocentric capture with extended consent and child-safety review — the egocentric kitchen video and egocentric data licensing specs anchor that workflow. Retail and last-mile programs (shelf-stocking, fulfillment, delivery-handoff) need outdoor and semi-public capture with location releases; we route those through industrial egocentric video. Manipulation programs targeting VLA training rely on hand-pose and force-torque streams aligned to language instructions. Humanoid foundation-model training spans all four because the underlying GR00T-style architecture treats embodiment as a parameter, not a constraint [2], and multi-robot coordination is where most deployment-realistic demos live.

Truelabel's playbook for each: define the embodiment + environment up front, specify accepted-episode count, pre-share evaluation rubric with the supplier, gate scale-up on first-batch QA. The egocentric warehouse video sourcing spec is a good first-look at how those constraints translate to a buyer brief.

  • Warehouse: bin-picking, packing, kitting → start at 5k accepted episodes per task
  • Household: laundry, cooking, cleaning, kid-care → egocentric + extended consent
  • Retail / last-mile: shelf-stocking, delivery handoff → location releases required
  • Manipulation / VLA: hand-pose + force-torque + language-aligned demos
  • Humanoid: whole-body teleoperation across the above verticals

Supplier vetting and delivery quality bars

Marketplace value comes from rejecting bad batches before they reach the buyer, not from raw supplier count. Truelabel's vetting covers four gates. Capture-rig fitness: the rig matches the embodiment (e.g., GoPro Hero 12 for first-person manipulation, multi-camera + RGB-D for tabletop policy work). Provenance and consent: each session carries contributor consent, location release, and metadata sufficient for downstream license verification [8]. Format fidelity: deliveries use LeRobotDataset v3 or RLDS so buyers can ingest without bespoke ETL [7] [10]. Quality calibration: a first-batch eval pack runs against the buyer's evaluation rubric before broader collection begins [11].

Suppliers who pass appear in our 100+ vetted-partner registry; the rest stay in the queue with feedback until they meet the bar.

Related pages

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

Physical AI data providers: criteria and optionsRelated pageData provenance for physical AIRelated pageWhat is physical AI training data?Related pageRobot training data marketplaceBuyer conversion pageSourcing egocentric kitchen videoRelated pageSourcing egocentric warehouse videoRelated pageSourcing egocentric workshop videoRelated pageSourcing industrial egocentric videoRelated page

External references and source context

  1. 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 ↩
  2. NVIDIA GR00T N1 technical report

    NVIDIA GR00T N1 demonstrates that humanoid foundation models require heterogeneous data pyramids spanning teleoperation, egocentric video, and synthetic generation — pushing buyers toward marketplace sourcing over single-vendor capture.

    arXiv ↩
  3. Figure + Brookfield humanoid pretraining dataset partnership

    Figure AI's partnership with Brookfield to deploy humanoids in commercial logistics facilities creates sustained demand for thousands of hours of whole-body teleoperation and warehouse manipulation data.

    figure.ai ↩
  4. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Open X-Embodiment aggregates 527 skills across 22 robot embodiments, exposing the heterogeneity gap that proprietary buyers fill with marketplace-sourced exclusive capture.

    arXiv ↩
  5. Scale AI: Expanding Our Data Engine for Physical AI

    Physical AI teams need custom robotics data beyond generic labeling programs.

    scale.com ↩
  6. 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 ↩
  7. 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 ↩
  8. 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 ↩
  9. 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 ↩
  10. RLDS: Reinforcement Learning Datasets

    Episode-based robot-learning datasets need canonical formats that preserve observations, actions, rewards, discounts, and metadata at step level.

    GitHub ↩
  11. 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.

Why has physical AI demand grown 3.5× in the last year?

Three forcing functions converged in 2025–2026. Humanoid foundation models like NVIDIA GR00T N1 (March 2025) established that whole-body policies need heterogeneous data pyramids spanning teleoperation, egocentric video, and synthetic generation. Commercial humanoid deployments (Figure AI × Brookfield in logistics, Agility Digit in warehousing) converted research demand into procurement demand. And Open X-Embodiment exposed the heterogeneity gap — 527 skills across 22 embodiments is still insufficient for production buyers who need exclusive capture for the embodiment and task distribution they actually deploy.

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 like Open X-Embodiment, DROID, and BridgeData V2 are useful baselines, but production robotics teams typically need data shaped to a deployment context — embodiment, sensor stack, task distribution, and environment that match what they're shipping.

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. The first-batch eval pack runs against the buyer's evaluation rubric before broad collection begins, so misalignment surfaces at the smallest possible cost.

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. Buyers can also mix: an OTS request for warehouse bin-picking baselines plus a NET_NEW request for the specific embodiment and packaging the buyer's deployment uses.

What sub-verticals does the marketplace cover?

Warehouse and commercial logistics (bin-picking, packing, multi-robot coordination), household and domestic tasks (laundry, kitchen, cleaning, kid-care), retail and last-mile (shelf-stocking, fulfillment, delivery handoff), manipulation programs targeting VLA training (hand-pose + force-torque + language-aligned demos), and humanoid foundation-model training that spans all four. Each sub-vertical has its own embodiment, environment, and licensing profile — playbooks per vertical at /solutions.

What delivery formats does truelabel support?

Primary formats are LeRobotDataset v3 (Parquet-based, schema for multi-modal time-series, sensorimotor signals, multi-camera video) and RLDS (canonical episode-step representation with observations, actions, rewards, discounts, metadata). Custom formats are available for buyers with downstream pipelines built against other schemas, but defaulting to LeRobot or RLDS reduces buyer-side ingestion overhead.

How does truelabel handle consent and licensing for egocentric data?

Each egocentric session ships with a contributor consent artifact (commercial training use, perpetual, worldwide, with revocation terms documented), a location release where applicable, and metadata sufficient for downstream license verification. The distinction matters because public egocentric corpora — Ego4D, EPIC-KITCHENS, HOI4D — often carry CC BY-NC or research-only licensing that doesn't transfer to commercial model training.

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