What are the best robotics data providers in 2026?
Answer in brief: If you need new robot data collected for you, the best robotics data providers in 2026 are Scale AI, Encord and Appen. Scale runs robotics data factories, home collectors and in-field deployments, and says more than 1,000 hours of demonstration data reach its platform every day. Encord combines thousands of trained in-field operators, Bay Area teleoperation labs and UMI gripper capture with its own data platform. Appen records sensor-synchronized human demonstrations in purpose-built kitchen, workshop, assembly and logistics facilities. Choose Guardian AI if you need provable provenance and remote teleoperation of your own robots, or Toloka for self-serve egocentric collection plus licensable UMI and teleop data. If you already have sensor logs and only need labels, pair a collector with Sama or Segments.ai.
Top pick: Scale AI Physical AI Data Engine. It has the broadest published collection footprint of any provider here. You can choose teleoperated bimanual arms, the robotless Scale Harness egocentric rig, or your own hardware operated by Scale, recorded in data factories, homes and operating businesses. Datasets go through Scale's own policy fine-tuning and evaluation before they are listed. Read the Scale AI review ·
Last updated: 2026-11-09
This guide covers one job: hiring a provider to collect new robot data for you through teleoperation fleets, data factories or egocentric capture. We compare providers on three things. Embodiments: which robot or human rig records the data. Scale: the volume and network size the provider publishes. Delivery: formats, cadence and QA. This is independent research based on what each company publishes on its own website. We did not test any of these services. See the methodology for how we ranked them. If you need perception labeling of logs you already have, see bestroboticsdatacompanies.com. For off-the-shelf datasets, see bestaidatasets.com.
The ranked list
1. Scale AI (Physical AI Data Engine)
What it is: A full-stack physical AI data program. It runs robotics data factories, uses home-based collectors and does on-site collection at operating businesses.
Best for: Robotics foundation-model labs and well-funded humanoid and manipulation teams that need high volumes from many robot types (embodiments).
Collection profile: Embodiments are teleoperated bimanual manipulators, the robotless Scale Harness egocentric rig, and your own hardware operated by Scale. On scale, Scale says it can grow to thousands of collectors and that 1,000+ demonstration hours are uploaded every day. Delivery goes through Scale's platform with grounding annotations. File formats are not published.
Strengths: In-house robotics researchers help write data specifications. Collection cells are validated in its San Francisco R&D lab before global rollout. Datasets are policy-tested before listing. It names Generalist and Physical Intelligence as platform users and is SOC 2 Type II and ISO 27001 certified.
Drawbacks: Sales-led and aimed at enterprises. There is no public pricing, and the white-glove model may be more than a small team or a narrow job needs.
Pricing: Custom quote (book a demo).
2. Encord
What it is: A data platform plus data-as-a-service. Encord calls itself an end-to-end physical AI data partner covering collection, curation, annotation and deployment feedback.
Best for: Teams that want collection and labeling in the same tool their engineers already use, especially for VLA (vision-language-action) work and LiDAR.
Collection profile: Encord collects four data types: embodiment-specific data on your own robot, leader/follower teleoperation, egocentric head and wrist video at 1080p/30FPS, and UMI handheld-gripper data. It says it has thousands of trained in-field operators, plus Bay Area lab pods for kitchen, laundry and industrial tasks. Data flows straight into its platform, with native MCAP and ROSBAG support.
Strengths: Protocols are piloted in Encord's own facilities before scaling, and collection can also run on-site at your factory or warehouse. Field failures are captured through remote teleoperation and fed back into collection. VPC and on-premise deployment are available, and Encord reports SOC 2 Type II, HIPAA and GDPR certification. It partners with hardware vendors, including teleoperation arm and biometric sensor providers, to fill gaps in a training set.
Drawbacks: Collection is scoped and piloted project by project, so costs and lead times are not visible upfront. Its operator network is described as 'thousands' rather than with specific hours.
Pricing: The platform has Starter, Team and Enterprise tiers (no prices listed). Collection is quoted per project.
3. Appen
What it is: A long-established AI data company with a dedicated physical AI line: egocentric capture, robotics demonstrations, sensor fusion, LiDAR annotation and world-model data.
Best for: Manipulation and humanoid teams that want controlled capture environments and strong participant consent processes.
Collection profile: Embodiments are human demonstrations on stereo egocentric rigs with wrist-pose and gripper-state sensors. Appen reports a contributor network of 1M+ people in 170+ countries. Every dataset ships with intrinsic/extrinsic calibration and sub-millisecond alignment metadata.
Strengths: Purpose-built kitchen, workshop, assembly and logistics facilities. All streams are recorded to a shared clock. QA uses multi-pass review, inter-annotator agreement and gold-standard validation.
Drawbacks: Its demonstrations are human-performed, not native robot telemetry from your own robot. The off-the-shelf physical AI catalog is still small: robot-vacuum-view images, CAD files and license-plate annotations.
Pricing: Custom quote. Off-the-shelf datasets are licensed through its catalog.
4. iMerit
What it is: A managed annotation company with a robotics practice and an egocentric video collection service.
Best for: Perception teams in agriculture, warehouse, delivery, medical and industrial robotics that need large, consistent 2D/3D labeling, plus first-person video capture.
Collection profile: Collection uses wearable cameras. One published project recorded 200 hours in homes using Meta Quest 3 headsets. Footage is captured by iMerit's full-time employees and iMerit Scholars under standardized protocols and delivered raw or annotated.
Strengths: 6,000+ annotators and 10+ delivery centers. Its published Carbon Robotics case study reports nearly 1 million images annotated. It holds ISO 27001 and SOC 2.
Drawbacks: We found no public teleoperation offering. Its collection examples focus on household activities.
Pricing: Custom quote.
5. Toloka
What it is: An AI data company whose physical AI line provides data, demonstrations, annotation and evaluation.
Best for: Teams that want ready-to-license egocentric, UMI or teleoperation datasets, or fast self-serve custom collection.
Collection profile: Toloka's catalog lists UMI capture (a head camera plus two gripper cameras, 100 Hz IMU, 6-DoF pose) and VR-driven bimanual and single-arm teleoperation (25 Hz state/action). Its self-serve egocentric collection draws on 200,000+ contributors in 100+ countries, and Toloka says the first batch arrives in about 3 hours. Delivery is MCAP plus JSON for UMI and MP4 plus JSON for video.
Strengths: Self-serve with no minimums. The catalog lists 25,000+ hours of egocentric video, and Toloka actively sources teleop and UMI recordings from data partners. Its RoboBILT framework evaluates data and policy quality.
Drawbacks: Standard license terms and pricing are not public. Volume and QA claims are self-reported.
Pricing: Self-serve projects. Managed projects are priced after scoping, and dataset licenses are negotiated.
6. Defined.ai
What it is: An ethical AI data marketplace plus custom collection. It expanded its physical AI robotics offering on January 5, 2026.
Best for: Buyers who want to browse and buy off-the-shelf multimodal robotics datasets, or commission multi-sensor collections.
Collection profile: Collections cover human demonstrations, telerobotics, motion capture, force/torque, LiDAR and radar. It reports 1.6M+ crowd members across 150+ markets, and one customer quote describes 225 hours of annotated demonstration data.
Strengths: Covers images, video, annotated motion, 3D point clouds, LiDAR, radar, motion capture, force/torque, and human-demonstration and telerobotics data. Being a marketplace makes buying faster. It lists ISO 27001/27701 and ISO 42001 certification.
Drawbacks: Its public robotics material is mostly an announcement and catalog. It has less detail on capture facilities or QA for robotics specifically.
Pricing: Marketplace dataset purchases, or bespoke quotes.
7. Shaip
What it is: A managed data company offering physical AI collection and annotation: VLA, egocentric, manipulation and grasping, activity recognition, and safety labeling.
Best for: Teams that need detailed manipulation labels, including failure and recovery grasps, and safety-zone annotation.
Collection profile: Shaip reports 30K+ vetted collectors in 50 cities and 100K+ egocentric and demonstration clips captured. One published egocentric program collected 30,000 hours with head-mounted smartphones.
Strengths: Instruction-action pairs, grasp-pose and contact-force labels, and human-proximity labels referencing ISO 10218 and ISO/TS 15066. Work follows a scope, then pilot, then scale process. It is ISO 27001 certified.
Drawbacks: Its site shows SOC 2 Type II as 'ready' in one badge but lists it as held elsewhere, so ask for the report. Its scale figures are self-reported.
Pricing: Custom quote, usually starting with a pilot.
8. micro1
What it is: A human-data company offering expert-demonstrated robotics data and a robot deployment lab.
Best for: Policy-learning teams that need skilled-task demonstrations such as repair, assembly, logistics and field operations.
Collection profile: First-person (POV) and third-person capture of domain experts, with synchronized stereo video, IMU and multi-camera data. It also runs a controlled 'data factory' where it deploys robots, captures interaction data and evaluates them.
Strengths: Dense action-level annotations for VLA training, plus the Flow annotation engine it described in July 2026.
Drawbacks: Little public detail on volumes, formats or certifications for robotics, and no named robotics customers.
Pricing: Not public. Sales-led.
9. Guardian AI
What it is: A specialist in verified egocentric capture and remote teleoperation, with operations in Asia and the US.
Best for: Labs whose legal or data teams need contributor-level consent and proof that footage is authentic. Also teams that want demonstrations on their own robots without hiring operators.
Collection profile: Egocentric capture uses a stereo head camera and IMU. Its trained operators can also drive your robots remotely while the hardware stays at your site. Delivery comes in weekly batches in LeRobot, RLDS, HDF5 or MCAP, each with a QA report and per-clip manifests.
Strengths: Clips are signed on the recording device. Provenance tiers P0–P3 include IMU-versus-video consistency checks and screens for synthetic or re-filmed footage. Labels include 3D hand pose, 6-DoF camera trajectory and success, failure and recovery outcomes.
Drawbacks: A young company with limited public customer evidence. Collection is concentrated in Asia and the US.
Pricing: Price per finished hour, quoted after scoping. The paid pilot has a published minimum of 20 finished hours per week.
10. Claru
What it is: A training-data company focused on physical AI, offering curated and custom real-world video for robotics, VLAs and world models.
Best for: World-model and VLA labs that need very large volumes of egocentric and activity video with enrichment.
Collection profile: Capture uses wearable and smartphone cameras. One household program mounted cameras at roughly humanoid height. Claru reports 34,000+ contributors in 70+ countries. Its lab dataset page lists RLDS, HDF5, WebDataset, zarr and LeRobot delivery.
Strengths: Clips ship with depth maps, pose estimation, segmentation masks and metadata. Claru says it has captured 1.5M+ egocentric videos for one world-modeling lab.
Drawbacks: The offering is video-first. Its official site does not detail native robot telemetry, and its figures are self-reported.
Pricing: Custom scoping (book a call).
11. Sama
What it is: A managed annotation and model-validation company with a robotics and manufacturing practice. Pair it with a collector.
Best for: Industrial robotics, cobot, AMR (autonomous mobile robot) and palletization teams with complex multi-sensor rigs.
Strengths: Handles up to 14 sensors (camera, LiDAR, radar, ultrasonic), with SLAM-assisted point-cloud accumulation, ego-motion compensation and medium-length sequence tracking. It offers a written quality guarantee that starts at 95% and can reach 99.5%. Model-validation services identify which data is missing.
Drawbacks: We found no first-party physical data collection on its robotics pages.
Pricing: Custom quote.
12. Segments.ai
What it is: A multi-sensor labeling platform for robotics and autonomous vehicles, with access to labeling partners. Pair it with a collector.
Best for: Robotics engineers labeling their own camera and point-cloud data, such as off-road, delivery and maritime robots.
Strengths: Labels 2D and 3D data together with consistent track IDs. Projects cuboids from 3D to 2D. Merged point clouds and batch mode. Handles point clouds of unlimited size on higher plans.
Drawbacks: Labeling only. It does not collect demonstrations or footage.
Pricing: 14-day free trial. Plans are custom quotes, and the Core plan starts at 3,600 labeling-usage hours a year, which Segments.ai equates to $2.67/hour.
Comparison table: embodiments, scale and delivery
| # | Provider | Collects for you | Embodiments / rigs | Published scale | Delivery | Pricing |
|---|---|---|---|---|---|---|
| 1 | Scale AI | Yes: factories, homes, in-field | Teleop bimanual arms; Scale Harness; your hardware run by Scale | 1,000+ demo hours/day | Scale platform; formats not published | Quote |
| 2 | Encord | Yes: field operators + Bay Area labs | Your robot; leader/follower teleop; egocentric; UMI | Thousands of trained operators | Encord platform; MCAP/ROSBAG native | Platform tiers; collection quoted |
| 3 | Appen | Yes: purpose-built facilities | Human demos; stereo egocentric, wrist pose, gripper state | 1M+ contributors, 170+ countries | Calibration + sub-ms sync metadata | Quote / dataset license |
| 4 | iMerit | Yes: egocentric video | Wearable cameras (e.g. Meta Quest 3) | 200-hour in-home project published | Raw or annotated | Quote |
| 5 | Toloka | Yes: self-serve + managed | Egocentric; UMI; VR teleop arms | 200,000+ contributors; 25,000+ egocentric hours in catalog | MCAP+JSON (UMI); MP4+JSON (video) | Self-serve / scoped / license |
| 6 | Defined.ai | Yes: bespoke multi-sensor | Human demos; telerobotics; mocap; force/torque | 1.6M+ crowd, 150+ markets | Marketplace or bespoke | Marketplace / quote |
| 7 | Shaip | Yes: egocentric + demos | Head-mounted smartphones; VR headsets | 30K+ collectors, 50 cities | Format agreed at scoping | Quote + pilot |
| 8 | micro1 | Yes: experts + robot lab | POV/third-person experts; stereo, IMU, multi-camera; robots in its lab | Not published | Not published | Not public |
| 9 | Guardian AI | Yes: Asia + US | Stereo head cam + IMU; remote teleop of your robots | Pilot minimum 20 finished hrs/week | LeRobot, RLDS, HDF5, MCAP; weekly | Per finished hour, quoted |
| 10 | Claru | Yes: global network | Wearable/smartphone egocentric | 34,000+ contributors, 70+ countries | RLDS, HDF5, WebDataset, zarr, LeRobot | Scoped |
| 11 | Sama | No (labels your logs) | Up to 14 sensors labeled | Not applicable | API, CLI, webhooks | Quote |
| 12 | Segments.ai | No (labeling platform) | Camera + point cloud labeling | Not applicable | Python SDK; ML framework exports | 14-day trial; Core from 3,600 hrs/yr |
'Not published' means we found no public claim. It does not mean the provider cannot do it.
How to choose a robotics data provider that collects for you
- Decide which data type your model needs. Policy learning on a specific robot needs teleoperation episodes from that robot type. Representation learning and world models can use egocentric human video. Perception stacks need labeled LiDAR and camera data.
- Match the embodiment. Ask whether the provider can operate your robot (Scale, Encord, Guardian AI), uses its own teleop arms, or captures human demonstrations that you will need to retarget.
- Check whether action signal is included. Plain video gives a robot little to learn from. Ask for 3D hand pose, wrist pose, gripper state, camera trajectory and calibration data.
- Confirm delivery formats and cadence. Ask for MCAP, ROSBAG, LeRobot, RLDS or HDF5, synchronized timestamps, calibration files and a batch schedule.
- Audit consent and rights. Ask for contributor-level consent records, site permissions, bystander handling and commercial training rights.
- Run a paid pilot with written acceptance criteria before you commit to volume.
See our robotics data pricing guide for how each vendor charges.
FAQ
What is a robotics data provider?
A company that collects, labels or licenses data used to train and evaluate robots. This includes teleoperation demonstrations, egocentric (first-person) video, multi-sensor logs, and annotations such as action segments, hand pose and 3D cuboids. This guide focuses on providers that collect new data for you.
What is the difference between teleoperation data and egocentric video?
Teleoperation data is recorded while a human remotely controls a real robot, so it contains the robot's own joint states and actions. Egocentric video is filmed from a person's point of view. It is cheaper to scale but needs pose, depth or other enrichment to carry an action signal.
How much does robotics training data cost?
None of the collectors reviewed publish a numeric rate card for custom robotics collection. Prices are quoted based on data type, hardware, volume, annotation depth and QA level. Guardian AI quotes a price per finished hour, with a pilot minimum of 20 finished hours per week. Toloka offers self-serve egocentric collection with no minimums. Segments.ai publishes a labeling-usage rate of $2.67/hour on its Core plan.
Can I buy off-the-shelf robotics datasets?
Yes. Toloka, Defined.ai, Appen and Claru all offer licensable datasets. Check the license terms and the robot type the data was recorded on. For a dedicated dataset comparison, see bestaidatasets.com.
Which provider is best if I only need LiDAR and camera labeling?
Sama, Segments.ai, iMerit and Encord all offer strong multi-sensor 2D/3D annotation. Segments.ai is a self-serve platform, and Sama and iMerit are managed services. For a full perception-labeling comparison, see bestroboticsdatacompanies.com.
What file formats should robotics data be delivered in?
Common formats are MCAP and ROSBAG for sensor logs, and LeRobot, RLDS or HDF5 for learning episodes. Encord supports MCAP and ROSBAG natively. Guardian AI delivers in LeRobot, RLDS, HDF5 and MCAP. Claru lists RLDS, HDF5, WebDataset, zarr and LeRobot.
How can I verify that the data is real and consented?
Ask for per-clip manifests, consent records and duplicate or synthetic screening. Guardian AI publishes its P0–P3 provenance tiers. Appen and Claru describe their consent and licensing processes.
Related guides: bestroboticsdatacompanies.com (broader robotics data and perception labeling) · bestaitrainingdataproviders.com (AI training data in general) · bestaidatasets.com (off-the-shelf datasets)
Methodology · About · Pricing guide · Get featured
Named on this page as a provider? Join the network for a linked profile, Visit buttons and featured placement.