What are the best robotics data companies in 2026?

Answer in brief: For teams training robot policies, VLAs and humanoid foundation models, the best robotics data companies in 2026 are Scale AI (top pick), Labelbox Terra and Encord. All three record teleoperation and egocentric demonstrations, then annotate and deliver them. If you want to license data off the shelf, choose Graviti. For richly instrumented or provenance-verified first-person capture, choose Firsthand or Guardian AI. For LiDAR and multi-sensor perception labels, choose iMerit, Segments.ai or Kognic.

Last updated: 2026-11-09

Top pick: Scale AI Physical AI Data Engine. It offers the broadest collection footprint on this list: data factories, home collectors and in-field deployments. Collection options are teleoperated bimanual robots, the robotless Scale Harness, or your own hardware run by Scale. Scale says more than 1,000 hours of demonstrations reach its platform every day, and that each dataset goes through its own policy fine-tuning and evaluation before it is listed. Read the Scale AI review ·

For most robotics and humanoid teams, the strongest choices are Scale AI (Physical AI Data Engine), Labelbox Terra and Encord. All three collect teleoperation and egocentric demonstrations and also annotate and deliver them. If you want datasets you can license off the shelf, look at Graviti. If you mainly need verified first-person capture, look at Firsthand or Guardian AI. For multi-sensor and LiDAR perception labeling, look at iMerit, Segments.ai and Kognic.

This is independent research. We compiled the list from each company's own website and current public sources. We have not tested these products or run any pilots with these vendors. See how we ranked them. This site ranks companies for robot-learning teams, meaning teams that turn demonstrations into policies. For broader AI training-data vendors, see our sister guide Best AI training data providers.

The ranked list

1. Scale AI: Physical AI Data Engine

What it is: A managed service that collects real-world robot demonstration data from Scale's robotics data factories, distributed home collectors and in-field commercial deployments. Demonstrations are annotated with grounding labels.
Best for: Well-funded foundation-model and humanoid labs that need large volumes of data across many embodiments.
Strengths: Data comes from teleoperated bimanual manipulators, the robotless egocentric “Scale Harness”, or your own hardware run by Scale. Scale says more than 1,000 hours of demonstration data reach its platform each day, and that datasets go through its own policy fine-tuning and evaluation before they are listed. It names Generalist and Physical Intelligence as partners and states SOC 2 Type II and ISO 27001 certification.
Drawbacks: Sales-led with no public pricing. Built for large programs, so probably too much for small teams.
Pricing model: Custom quote (book a demo).
Full review: Scale AI review

2. Labelbox: Terra

What it is: Labelbox's robotics data engine. It records egocentric and teleoperated demonstrations on purpose-built rigs, then validates them and delivers data for pre-training, post-training and evals.
Best for: Teams that want one episode format from pre-training through evaluation, and collection steered toward coverage gaps.
Strengths: Seven hardware-synchronized rig types, from ego mono up to trio plus instrumented gripper, plus teleoperation with joint states and actions. Up to 4 synced camera streams per episode. A “diversity engine” redirects collection toward under-covered environment and task combinations. Labelbox says Terra had produced more than 1 PB as of January 2026. It is embodiment-agnostic and offers standardized evals scored against your policy.
Drawbacks: Collection runs through Labelbox's Alignerr network and third-party partners, so ask who records your data. Pricing is not public.
Pricing model: Custom program scoping.
Full review: Labelbox Terra review

3. Encord

What it is: A data platform for annotation, curation and evaluation, plus managed data-as-a-service. Encord collects teleop and egocentric data in its own facilities.
Best for: Teams that want collection, multi-sensor annotation and curation tooling in one place, either self-serve or managed.
Strengths: RGB, depth, LiDAR and force/torque labeled in one interface. Offers 6-DoF pose and keypoint labeling for grasps, embedding-based search for edge cases, and video-language-action workflows. Supported data types include UMI, stereo, teleop and egocentric video. Encord says Pickle Robot saw a 30% improvement in annotation accuracy.
Drawbacks: On the pricing page, 3D/LiDAR/point cloud annotation is an add-on, and no tier lists a price.
Pricing model: Starter, Team and Enterprise tiers, plus managed services by quote.
Full review: Encord review

4. Graviti

What it is: Captures, annotates and licenses physical-AI datasets, from a public catalog or as custom programs.
Best for: Buyers who want to license existing data now and read a spec before talking to sales.
Strengths: Graviti lists three catalog datasets. Ego-Exo Workplace Tasks has 4,300+ hours, 4 synchronized views at 30 fps, and is available now. Egocentric Household Activities has 10,000+ hours from 2,400+ contributors. Bimanual Teleoperation Trajectories has 120,000+ episodes across 180 task families. Delivery formats are LeRobot, RLDS, WebDataset, Parquet and JSONL. Every contributor signs a revocable agreement covering AI training, and licenses are perpetual.
Drawbacks: The teleoperation dataset is early access. The company also sells LLM data, so robotics is not its only focus.
Pricing model: Licensing by quote. Evaluation samples on request.
Full review: Graviti review

5. Firsthand

What it is: Collects custom egocentric video datasets for embodied AI and robotics, to your specification.
Best for: Teams that need richly instrumented first-person human data with tight synchronization.
Strengths: Each episode from its instrumented rig ships ten synchronized streams: 4K head camera, two wrist cameras, depth, IMU, 3D hand and body pose, segmentation and action labels. Firsthand reports a 1.12 ms median cross-stream sync error on that rig and 10,585 validated hours delivered. The buyer owns the commercial license. Sample clips can be downloaded without a form. Delivery formats are RLDS, LeRobot and WebDataset.
Drawbacks: This is human egocentric data, not robot teleoperation. It needs retargeting before a policy can learn actions from it. A second, lighter 360-camera tier does not include depth, wrist views or the sync guarantee.
Pricing model: Billed per validated hour. The rate is not published.
Full review: Firsthand review

6. Guardian AI

What it is: Verified egocentric capture across Asia and the US, plus remote teleoperation: Guardian's operators drive your robots.
Best for: Teams whose legal or data teams require clip-level provenance and consent records, and teams without in-house teleop operators.
Strengths: Clips are signed on the device that records them, checked for IMU and visual consistency, and screened for synthetic or re-filmed footage. Four provenance tiers, P0 to P3. Outputs include 3D hand pose, 6-DoF camera trajectory, contact labels and success/failure/recovery labels. Delivery formats are LeRobot, RLDS, HDF5 and MCAP. Robots stay at your site.
Drawbacks: Little public track record. The capture reel on its site uses open-dataset clips (Build AI Egocentric-10K), not Guardian's own capture. The paid pilot requires at least 20 finished hours per week.
Pricing model: Price per finished hour, quoted after a scoping call.
Full review: Guardian AI review

7. Appen

What it is: A long-running AI training-data vendor (it cites 30 years of data provenance) with a dedicated Physical AI line.
Best for: Enterprises that already buy from Appen and want egocentric, LiDAR, sensor-fusion and demonstration data from one vendor.
Strengths: Stereo egocentric capture with synchronized wrist pose and gripper state, recorded in on-site kitchen, workshop, assembly and logistics facilities. It also covers human task demonstrations with hand trajectories, camera/LiDAR/radar sequences, LiDAR annotation and world-model data. Appen cites a contributor network of 1M+ people in 170+ countries.
Drawbacks: Physical AI is one line in a broad portfolio. No public pricing, and delivery formats are not stated.
Pricing model: Enterprise quote.
Full review: Appen review

8. iMerit (an EXL company)

What it is: A managed annotation and validation provider for robotic perception, running on its Ango Hub platform.
Best for: Perception teams in warehouse, agriculture, delivery, medical or household robotics that need high-volume 2D/3D labeling.
Strengths: Multi-sensor fusion, 3D point clouds, panoptic segmentation and object tracking. iMerit says it has 6,000+ annotators and 10+ delivery centers, and lists ISO 27001, SOC 2 and HIPAA compliance. It reports that nearly 1 million images were annotated for Carbon Robotics' precision weeding.
Drawbacks: Focused on perception annotation. Public pages say little about collecting demonstration data.
Pricing model: Custom quote.
Full review: iMerit review

9. Segments.ai

What it is: A multi-sensor labeling platform (software) for robotics and AV teams.
Best for: Teams with their own labelers who need point-cloud and image labeling tied together.
Strengths: Labels 3D and 2D together, keeps track IDs consistent across sensors, projects 3D cuboids into images, merges point clouds and offers ML-assisted tracking. Has a 14-day free trial and free academic licenses.
Drawbacks: It is a tool, not a data supplier. Labeling-as-a-service comes through partners.
Pricing model: Custom quotes on annual labeling-hours plans. Core starts at 3,600 hours/yr, which Segments.ai says equals $2.67/hour. Fusion starts at 5,000 hours/yr, and Enterprise includes 150,000+ hours/yr.
Full review: Segments.ai review

10. Kognic

What it is: An annotation platform plus specialist workforce for camera, LiDAR and radar data. It was founded in Gothenburg in 2018.
Best for: Autonomous mobile systems and AV-style perception, planning and end-to-end training data.
Strengths: Kognic reports 100M+ annotations across 120+ projects. It is expanding into trajectory ranking, language grounding and VLA annotation. You can buy it as a managed service, a platform license or a hybrid.
Drawbacks: Positioned around automotive, so it is not aimed at manipulation or humanoid demonstrations.
Pricing model: Quote.
Full review: Kognic review

11. Robgence

What it is: End-to-end physical-AI data: egocentric capture, teleoperation, motion capture, multimodal annotation, and synthetic/sim-to-real data.
Best for: Humanoid and VLA teams that want broad modality coverage and fast pilots.
Strengths: Delivers natively in LeRobot v2/v3, RLDS/RT-X and HDF5/robomimic, and offers a policy smoke test. Pilots take two weeks. It has an operator network in Latin America and claims 20,000+ operators in 50+ cities.
Drawbacks: Its scale figures are self-reported, and we could not verify them independently. No public pricing.
Pricing model: Quote after a pilot.
Full review: Robgence review

12. Tacit Robotics

What it is: Large-scale collection of egocentric video, teleoperation trajectories and handheld UMI gripper demonstrations.
Best for: Teams that want UMI data, which can transfer to robots without a robot on site, alongside teleop.
Strengths: Covers three capture modes in homes, kitchens, warehouses and shops. Tacit handles recruiting, hardware, sites, QA and delivery.
Drawbacks: Very little public detail on specs, formats or volumes.
Pricing model: Commissioned datasets, by quote.
Full review: Tacit Robotics review

13. Rimb

What it is: A data platform that turns raw egocentric and robot footage into rights-cleared, training-ready episodes. It also collects first-person video through its phone app.
Best for: Teams that already have footage and need it annotated into episodes, or want a cheap trial.
Strengths: Annotates object tracks, hand-object interactions, hand keypoints, captions and depth. Exports LeRobot v2.1/v3.0, RLDS or MCAP. Offers five free annotated episodes in 7-10 days.
Drawbacks: Capture runs on contributors' phones, so check whether it gives you the sensor signal you need. An early-stage product (the app is at version 0.10.0).
Pricing model: Free sample, then quote.
Full review: Rimb review

Comparison table

#	Company	Type	Teleop	Egocentric	LiDAR / sensor fusion	Stated formats	Public pricing
1	Scale AI	Managed collection + annotation	Yes	Yes	Not stated on page	Not stated	No
2	Labelbox Terra	Managed collection + evals	Yes	Yes	Not stated	Single episode format	No
3	Encord	Platform + managed services	Yes	Yes	Yes	Not stated	Tiers listed, no prices
4	Graviti	Catalog + custom	Yes (early access)	Yes	No	LeRobot, RLDS, WebDataset, Parquet	No
5	Firsthand	Custom egocentric	No	Yes	Depth + IMU	RLDS, LeRobot, WebDataset	Per validated hour (rate private)
6	Guardian AI	Verified capture + remote teleop	Yes	Yes	Stereo/depth + IMU	LeRobot, RLDS, HDF5, MCAP	Per finished hour (quoted)
7	Appen	Enterprise data vendor	Demonstration trajectories	Yes (stereo rigs)	Yes	Not stated	No
8	iMerit	Managed annotation	No	No	Yes	Not stated	No
9	Segments.ai	Labeling software	No	No	Yes	n/a	Partial (from $2.67/hr equivalent)
10	Kognic	Platform + workforce	No	No	Yes	Not stated	No
11	Robgence	End-to-end services	Yes	Yes	RGB/depth/IMU	LeRobot, RLDS, HDF5, MCAP	No
12	Tacit Robotics	Collection	Yes	Yes	Not stated	Not stated	No
13	Rimb	Annotation platform + phone capture	Annotates robot footage	Yes	Depth labels	LeRobot, RLDS, MCAP	Free 5-episode sample

How to choose a robotics data company

Name the signal your model needs. Policy post-training needs observation-action pairs, from teleop or UMI. Pre-training and world models can use egocentric or multi-view human video, but that video needs hand pose, depth or camera trajectory to carry an action signal. Perception models need labeled LiDAR, camera and radar.
Decide between buying and building. If you have labelers, a platform such as Segments.ai or Encord may be enough. If you need someone to record the data, choose a collection vendor.
Check embodiment fit. Ask whether teleop runs on your robots (Guardian, Scale's bespoke option) or on the vendor's rigs.
Insist on your format. LeRobot, RLDS, HDF5 and MCAP are the formats vendors most often name.
Audit consent and provenance. Ask for per-clip consent records, bystander handling and license terms, including whether rights are perpetual or exclusive.
Run a paid or free pilot with written acceptance criteria before you commit to production volume.

Frequently asked questions

What is robotics training data?

It is recorded real-world or simulated data that robot models learn from. Common types are teleoperated demonstrations (video plus joint states and actions), egocentric first-person video of people doing tasks, UMI handheld-gripper demonstrations, and labeled sensor data such as LiDAR point clouds.

Which company is best for humanoid teleoperation data?

Scale AI, Labelbox Terra and Encord all run teleoperation collection. Guardian AI sends remote operators to drive your own robots. Graviti offers a 120,000+ episode bimanual teleop dataset in early access.

What is the difference between egocentric video and teleoperation data?

Egocentric video shows a person doing a task from their own point of view. It is useful for pre-training, but it does not contain robot actions. Teleoperation data is recorded on a robot and includes the actions and joint states a policy can imitate directly.

How much does robotics training data cost?

Most vendors don't publish prices. Firsthand bills per validated hour and Guardian AI quotes per finished hour. Segments.ai, a labeling tool, says its Core plan works out to about $2.67 per labeling hour. Scale, Labelbox, Appen and others quote each project individually.

What file formats do robotics data vendors deliver?

The ones vendors name most often are LeRobot, RLDS, HDF5 (including robomimic), MCAP and WebDataset.

Can I license robotics data off the shelf?

Yes. Graviti publishes a catalog with specs and evaluation samples. Most other vendors collect data to order.

Should I use a labeling platform or a managed data service?

Use a platform if you have your own labelers and data. Use a managed service if you need data recorded, or need annotation capacity you don't have. Encord and Kognic offer both.

How do I verify the data is real and consented?

Ask for per-clip manifests and consent records. Guardian AI signs footage at capture and screens for synthetic or re-filmed clips. Graviti and Firsthand document written consent for every contributor.

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