Buyer's guide · Updated November 2026

Best Data Labeling Companies for Robotics (2026)

Robotics training data does not label itself. Whether you are annotating egocentric video, 3D sensor-fusion streams or robot-action captions, the company you pick decides whether your labels are consistent enough to train a policy. This guide compares eight labeling companies and platforms that work with physical AI teams — what each one actually does, how they engage, and who they fit.

Every vendor claim on this page is sourced from the vendor's own site (fetched November 2026) or from Claru's published program facts — see the claim ledger in docs/seo/claims/blog-best-data-labeling-companies-for-robotics.md.

The shortlist

Vendor	Type	Robotics annotation coverage	Engagement model	Best for
Claru	Capture + labeling	Egocentric collection; annotation and derived enrichment scoped per program	Program-based collection with agreed annotation scope	Robotics teams that need new labeled first-person data, not just a tool
iMerit	Managed labeling service	Computer vision, audio, LiDAR, sensor fusion; robotics validation	Managed teams; in-house dedicated workforce	High-volume multimodal annotation with managed QA
Scale AI	Managed labeling service	Robotics foundation-model data; expert evaluations	Enterprise programs	Frontier labs buying large, expert-heavy data programs
Appen	Crowd + managed service	Egocentric video, pose, gripper state and synchronized sensor streams	Platform + managed crowd (1M+ contributors)	High-volume, globally distributed annotation work
TELUS Digital	Managed labeling service	Video/image annotation; 3D sensor fusion, point-cloud segmentation	Managed AI community (1M+ annotators, 500+ languages)	Enterprises wanting a BPO-style labeling relationship
Surge AI	Expert human-data service	RL environments, rubrics, verifiers; expert-domain data	Expert workforce, RLHF and evaluations	Post-training and evaluation data with elite domain experts
Encord	Annotation platform	Multimodal annotation workflows; physical AI customers	SaaS platform + on-demand expert annotators	Teams labeling robot data in-house with their own workforce
Labelbox	Annotation platform	Labeling environment with agent automation; frontier-lab adoption	SaaS platform	ML teams standardizing labeling and post-training ops in one tool

Ordered by how directly each company serves robotics data work: capture-plus-labeling first, then managed services, then platforms you run yourself.

Need labeled robot data that does not exist yet? Claru collects egocentric video to your spec and delivers it labeled — depth, pose, segmentation, captions and action labels on the same timeline.

Scope a labeling program

How we compared

Robotics annotation coverage.Does the vendor's own site name the annotation types robotics actually needs — 3D/LiDAR fusion, trajectories, pose, video — or only generic image/text tagging?
Who does the work. Employees, managed crowd, on-demand experts, or your own team on a platform. Consistency differs enormously between those models.
Delivered artifact. What you actually receive: schemas, formats, QA process, and whether labels ride on data the vendor also collected.
Sourceability.We only state what each vendor's own pages support. No invented prices, no recycled comparison-copy claims.

Platform, managed service, or capture + labeling?

Labeling platforms (Encord, Labelbox) give you the tooling: annotation workflows, QA, automation, and increasingly agent-assisted labeling. You supply the workforce. This is the right call when you already have annotators and your bottleneck is process.

Managed labeling services (iMerit, Scale AI, Appen, TELUS Digital, Surge AI) supply both people and process, at enterprise program scale. The question to ask is who exactly labels robotics data — general crowds are cheap and inconsistent; expert pools cost more and hold a schema.

Capture + labeling providers (Claru) collect the data to your specification first — egocentric video, wearables, workplace scenes — then label it. If the data does not exist yet, no amount of annotation tooling fixes that.

Claru — capture + labeling for physical AI

Claru runs contributor programs that collect egocentric and workplace video to spec. Agree the annotation schema and any requested derived enrichment — such as depth, pose, segmentation or action labels — during calibration; define included outputs and their acceptance tests. Program-wide, the network has delivered 360,000+ accepted annotation hours from 34,000+ approved contributors in 70+ countries; the household humanoid program alone holds 344,000+ accepted hours across 220+ distinct household tasks.

Published evidence covers different kinds of work: the caption-verification case records 44,555 first-pass verifications of temporal action captions on human video in 12 days. The separate household humanoid case reports 344,000+ accepted capture hours across 220+ distinct tasks from 31,000+ contributors. Agree commercial usage rights and the delivered artifacts for your program.

Evidence: caption verification case study, household humanoid case study. Related: best robotics data providers.

iMerit — managed expert annotation at scale

iMerit sells expert-led annotation and validation for "Computer Vision, Audio, LiDAR, and Sensor Fusion with guaranteed high quality and long-term consistency" — the multimodal mix robotics teams need. Its robotics pitch is explicit: "Structured training data and real-world scenario validation enhance robotic perception, navigation, and task execution in dynamic settings."

The workforce model differs from crowds: iMerit describes "a highly skilled in-house dedicated workforce" and a Scholars program of "25,000+ Domain Experts / 60+ Countries". Note the company is now part of EXL. The Ango hub is its annotation and model-evaluation platform.

Source: imerit.net, fetched 2026-11-11. Compare: iMerit alternatives.

Scale AI — robotics foundation-model data programs

Scale's homepage leads with physical AI partners: it is "Fuelling the next generation of robotic foundation models with real-world training data" for Physical Intelligence, and "Enabling scalable, real-world Physical AI for industrial robotics" for Universal Robots. Its evaluations side ("expert-level evaluations") matters for teams that need labeled data plus benchmarking.

Confirm the proposed program scope, price, included outputs and QA ownership against your requirements.

Source: scale.ai, fetched 2026-11-11. Compare: Scale AI alternatives.

Appen — the global crowd, plus physical-AI data types

Appen operates "a global crowd of more than 1 million contributors worldwide, speaking more than 235 languages", assembled from "170+ countries". Its physical-AI offering includes egocentric video, pose, gripper state and synchronized sensor streams.

Appen offers egocentric video synchronized with pose, gripper state and multi-sensor data. Confirm collection scope, schema-specific QA and acceptance criteria against your robotics program.

Source: appen.com, fetched 2026-11-11. Compare: Appen alternatives.

TELUS Digital — enterprise annotation with a 1M+ AI community

TELUS Digital (formerly TELUS International) fields a "1M+ Diverse global AI Community of annotators and linguists" across "500+ Annotation languages & dialects", claims "2B+ Labels annually", and lists "3D Sensor Fusion Annotation" including "point cloud segmentation" — directly relevant to robotics.

A good fit when you want a BPO-style relationship with enterprise governance around a large annotation workforce.

Source: telusdigital.com, fetched 2026-11-11. Compare: TELUS Digital alternatives.

Surge AI — expert human data for post-training

Surge AI is "a data labeling platform focused on the richness of AI" with an elite-expert positioning — "doctors, lawyers, investment bankers, Fields Medalists, Harvard professors" — and products spanning "RL Environments and Agents" and "Rubrics and Verifiers".

Robotics teams mostly meet Surge on evaluation and expert-domain data rather than bulk video annotation.

Source: surgehq.ai, fetched 2026-11-11.

Encord — annotation platform with physical-AI customers

Encord is the platform to "Manage, curate, and annotate AI data" with "customizable multimodal workflows", and can also connect "expert annotators, domain specialists, and managed collection pipelines". Its physical-AI customer roster includes Woven by Toyota, Agility Robotics, Forterra and Zipline.

Choose it when your team annotates in-house and needs tooling built for video and sensor data, with experts on tap for overflow.

Source: encord.com, fetched 2026-11-11. Compare: Encord alternatives.

Labelbox — the labeling environment frontier labs use

Labelbox "builds the environments frontier labs train AI on" and reports that "Over 90% of leading US AI labs post-train and evaluate on Labelbox". Its agent automation ("Recursion") runs background labeling jobs from a described spec.

A strong default for ML teams consolidating labeling and evaluation ops in one platform; robotics-specific workflows will still be yours to define.

Source: labelbox.com, fetched 2026-11-11. Compare: Labelbox alternatives.

Which one fits your program?

Data exists, team exists: buy a platform (Encord, Labelbox) and standardize your workflow.

Data exists, no workforce: buy a managed service (iMerit, Scale AI, Appen, TELUS Digital) — and stress-test robotics QA before scaling.

Data does not exist yet: buy capture + labeling (Claru). Collect to spec, label to schema, license commercially, one program.

Post-training and eval: expert human-data specialists (Surge AI) fit best.

Related buyer guides

Best robotics data providers (2026)

Capture-side comparison, vendor by vendor

Best VLA training data providers

Action labels and embodiment coverage

Manipulation trajectory data

Scoped collection for manipulation tasks

Frequently Asked Questions

What makes data labeling for robotics different from general annotation?

Robotics labeling often needs paired modalities — egocentric video with depth, 3D pose, segmentation and action labels on the same timeline — and consistent label vocabularies across thousands of clips. A vendor that can tag images well cannot necessarily deliver temporally consistent action annotations for a manipulation policy.

Should I use an annotation platform or a managed labeling service?

Use a platform (Encord, Labelbox) when you have your own workforce and want tooling, QA workflows and automation. Use a managed service (iMerit, Scale AI, Appen, TELUS Digital) when you need the people as well as the tooling. Use a capture-plus-labeling provider (Claru) when the data itself does not exist yet and must be collected to spec, then labeled.

Which robotics annotation types matter most for VLA training?

Action labels, trajectory and pose annotations, language captions of actions, and segmentation that survives occlusion. Hand-object interactions in first-person video are the hardest to keep consistent, which is why QA-verified caption programs exist — Claru recorded 44,555 first-pass temporal action-caption verifications on human video in 12 days on one robotics research program.

Can open datasets replace a labeling vendor?

Rarely for commercial training. Most large open egocentric datasets carry non-commercial research licenses (for example EgoDex under CC BY-NC-ND), so a commercial deployment usually needs commercially licensed data — collected or labeled under an explicit license.

How do labeling vendors price robotics work?

Most enterprise vendors price per program after scoping, not from a public rate card. Claru prices collection-plus-labeling programs per spec; expect every serious vendor to quote after seeing your annotation schema and volume.

What should I ask a labeling vendor before signing?

Ask for the exact delivered artifact (schema, format, frame alignment), who performs the work (employees, crowd, experts), the QA and acceptance process, licensing and exclusivity terms, and what happens when edge cases dominate. A vendor that cannot show a labeled sample from your domain is a risk at program scale.

Need labeled robotics data?

Claru collects it and labels it — one program

Egocentric and workplace video collected to your specification. Agree the annotation schema, requested derived enrichment, delivery artifacts and commercial usage rights. The whole contributor network spans 34,000+ contributors in 70+ countries.

Talk to the Claru team