Last updated: November 2026

Best Robotics Data Providers (2026)

Choosing a robotics data vendor is not like buying generic annotation. The wrong vendor — one built for text or third-person video — cannot produce what a modern robot policy needs. The shortlist below compares nine providers on capture modality, annotation depth, delivered artifact and engagement model, then each entry explains who it fits.

The shortlist, ranked

Vendor	Capture / annotation / platform	Modalities	Delivered artifact / format	Engagement model	Best fit
Claru	Capture + enrichment	Egocentric video (wearable/smartphone), workplace video; depth, pose, segmentation, optical flow, action labels, captions	Enriched datasets, licensed	Program-based collection	Robotics teams that need new, commercially licensed first-person data
Mecka	Capture + evaluation	First-person human activity (Egoverse); teleoperation fleets; annotation, QA, motion understanding	Dataset access or commissioned collection	Dataset access → task-specific collection → full data program	Teams bridging data and robot deployment workflows
XDOF	Datasets + robotic systems	Production-scale datasets for robotics labs; teleoperation (GELLO authors)	Datasets, systems, tooling	Research-lab partnerships	Frontier robotics labs needing production-scale data builds
Human Archive	Data + deployments	Human video/sensor data for Physical AI; robot deployments	Catalog data + deployment infrastructure	Direct engagement	AI labs scaling human data and robot deployment together
iMerit	Managed annotation	CV, audio, LiDAR, sensor fusion; robotics validation	Labeled data via Ango hub	Managed expert teams	High-volume multimodal annotation with managed QA
Scale AI	Managed annotation + eval	Robotics foundation-model data; industrial physical AI; expert evaluations	Program-scale labeled data + benchmarks	Enterprise programs	Frontier labs buying large data programs end to end
Appen	Crowd annotation	Egocentric video, pose, gripper state and sensor streams; 1M+ contributors	Labeled data at volume	Platform + managed crowd	Globally distributed, high-volume annotation
TELUS Digital	Managed annotation	Video/image, 3D sensor fusion, point-cloud segmentation	Labeled data at enterprise volume	Managed AI community (1M+)	Enterprise BPO-style annotation relationships
Surge AI	Expert human data	RL environments, rubrics, verifiers; expert-domain data	Post-training and evaluation data	Expert workforce	RLHF/eval data with elite domain experts

Ordered: physical-AI capture specialists first, then managed annotation vendors, then expert human-data specialists. Every row is sourced from the vendor's own site or Claru's published program facts (fetched November 2026).

Need new first-person training data rather than tooling? Claru collects egocentric and workplace video to spec and delivers it enriched — depth, pose, segmentation, captions and action labels on one timeline.

Scope a data program

How we compared

What they actually do. Collect new data, annotate yours, or both. The table states this per vendor because it is the first decision that matters.
Robotics modality depth. Egocentric video, teleoperation demonstrations, LiDAR/3D fusion, trajectory and action labels — named on the vendor's own pages or absent.
Delivered artifact. Enriched datasets vs labeled data vs platform access; formats and licensing included.
Engagement model. Program-based collection, managed services, crowd, or platform subscription.
Sourceability.Every claim is tied to a fetched vendor page or Claru's published program facts; the ledger is in docs/seo/claims/blog-best-robotics-training-data-companies.md.

What Makes Robotics Training Data Different

General AI training data — text corpora, image classification sets, third-person video — does not teach a robot how to move through a room or pick up an object. Robots operate through their own cameras in real time. The training data must match that perspective exactly: egocentric (first-person) viewpoints, synchronized with the physical actions the demonstrator performed, annotated with the language instructions that governed those actions.

Beyond viewpoint, robotics data needs enrichment layers that standard annotation platforms were never designed to produce. Depth maps give models spatial understanding for grasp planning. Pose estimation captures hand-object interaction geometry. Semantic segmentation identifies objects and their affordances. Without these layers, raw video footage cannot train a manipulation policy — it requires months of in-house pipeline engineering before any model can consume it.

Finally, licensing decides whether the data can ship. Most large open egocentric datasets carry non-commercial research licenses (EgoDex under CC BY-NC-ND; EPIC-KITCHENS under CC BY-NC with commercial use requiring a separate agreement). Commercially licensed capture is a different purchase category, and most robotics programs need both kinds of source.

Claru

Specialist

Egocentric-first collection, enrichment and delivery for physical AI

Claru is purpose-built for physical AI and robotics. Program-wide it has delivered 360,000+ accepted hours across its programs from 34,000+ approved contributors across 70+ countries. The household humanoid program alone covers 344,000+ accepted hours across 220+ distinct household tasks in 32 countries, with 126 tasks already past 100 hours and 62 past 1,000 hours. Specify the requested annotation schema and any derived enrichment — such as depth, pose, segmentation or action labels — for the collection program. The 2025 egocentric collection delivered 386K+ clips from three capture pipelines, and the separate human-video action-caption program recorded 44,555 first-pass verifications in 12 days. Visit Claru

Strengths

+Purpose-built for physical AI — not a generalist with a robotics SKU
+Annotation and derived enrichment scoped to the program
+Delivered-program evidence: 344K+ household hours, 220+ tasks, 126 tasks ≥100h, 62 tasks ≥1,000h
+Household humanoid and workplace case studies published with per-program numbers
+RekaDaily-10k: 10,200 processed hours released Apache 2.0 via Reka, collected on the Claru network
+Commercial licensing and consent documentation included in delivery

Limitations

–Collection programs are scoped per spec — no public self-serve catalog with checkout
–Focused on egocentric and workplace video, not LiDAR-first or warehouse-fleet telemetry

Best for: Robotics and embodied AI teams that need new, commercially licensed first-person training data with enrichment layers already computed.

Mecka

Specialist

Data, evaluation and deployment infrastructure for physical AI

Mecka builds 'the infrastructure for robots to learn about the real world and deploy reliably into commercial settings' — explicitly not a robot builder, but the data and integration layer. Its Egoverse dataset is 'built from real-world first-person activity across tasks, spaces, and environments'. The company raised a $60M Series B led by Sequoia. Teams can 'start with dataset access, commission task-specific collection, run model evaluations, or build a full data program across environments.' Visit Mecka

Strengths

+Direct physical-AI focus with a first-person human-activity dataset (Egoverse)
+Ladder of engagement: dataset access → commissioned collection → full data program
+Well funded ($60M Series B led by Sequoia)

Limitations

–Younger program track record than established annotation vendors
–Robot-deployment workflows are part of the offer — teams wanting pure data delivery should scope that boundary explicitly

Best for: Teams that want a physical-AI specialist bridging data capture, evaluation and deployment workflows.

XDOF

Specialist

Production-scale datasets for the world's leading robotics labs

XDOF partners 'closely with the world's leading robotics labs and most promising robotics companies to deliver production-scale datasets, advanced robotic systems, and the tooling needed to support the next generation of physical AI.' Its co-founders authored GELLO, 'now commonly deployed for teleoperation' — the low-cost teleoperation rig many robotics teams already use, which gives XDOF direct fluency in demonstration capture. Visit XDOF

Strengths

+Founded by the authors of GELLO — deep teleoperation and demonstration-capture credentials
+Production-scale dataset delivery for robotics labs
+Datasets, systems and tooling from one team

Limitations

–Research-lab orientation; enterprise program management is not the stated focus
–Public program-scale evidence is thinner than the large annotation vendors

Best for: Frontier robotics labs that need production-scale demonstration datasets built by teleoperation experts.

Human Archive

Specialist

Data and deployments infrastructure for Physical AI

Human Archive is 'a data and deployments infrastructure company for Physical AI', built by 'researchers from Stanford, Berkeley, NYU GRAIL, UPenn GRASP, JHU Robotics' and backed by Y Combinator. The company is 'currently working alongside leading AI labs to scale human video/sensor data and deploy robotics', with an off-the-shelf catalog and robot deployment offerings. Visit Human Archive

Strengths

+Strong research pedigree across top robotics labs
+Both human video/sensor data and robot deployment infrastructure
+Off-the-shelf catalog plus custom engagements

Limitations

–Early-stage relative to the enterprise annotation incumbents
–Catalog contents and licensing terms need scoping per engagement

Best for: AI labs scaling human data capture and robot deployments on the same roadmap.

iMerit

Annotation specialist

Expert-led annotation and validation, now part of EXL

iMerit provides 'annotation and validation for Computer Vision, Audio, LiDAR, and Sensor Fusion with guaranteed high quality and long-term consistency', with an explicit robotics use case: 'Structured training data and real-world scenario validation enhance robotic perception, navigation, and task execution in dynamic settings.' The workforce model is 'a highly skilled in-house dedicated workforce' — positioned against crowd-sourced annotation — with a Scholars program of '25,000+ Domain Experts / 60+ Countries'. iMerit is now part of EXL, and annotates on the Ango hub platform. Visit iMerit

Strengths

+Multimodal annotation coverage that matches robotics stacks (CV, audio, LiDAR, sensor fusion)
+In-house dedicated workforce model supports schema consistency
+Compliance posture stated (SOC2, ISO 27001, GDPR, HIPAA, TISAX)

Limitations

–Confirm the required egocentric collection scope with iMerit
–Robotics is one vertical among many; ask for robotics-relevant references

Best for: Teams with existing robotics data that need high-volume, managed annotation with long-term consistency.

Scale AI

Annotation + evaluation

Robotics foundation-model data at enterprise scale

Scale's own homepage leads with physical AI: 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 evaluation side — 'Benchmarking the frontier of AI capability with expert-level evaluations' — rounds out the data offer for teams that need labeled data plus benchmark evidence. Visit Scale AI

Strengths

+Named robotics partners at the foundation-model frontier (Physical Intelligence) and industrial robotics (Universal Robots)
+Expert-level evaluation services alongside annotation
+Enterprise program scale

Limitations

–Annotation and evaluation of existing/collected data — the collection program itself is scoped enterprise-side
–No public pricing; procurement is enterprise-paced

Best for: Well-funded teams buying large robotics data and evaluation programs from a single enterprise vendor.

Appen

Crowd generalist

A global crowd of 1M+ contributors, with physical-AI data types

Appen 'boasts a global crowd of more than 1 million contributors worldwide, speaking more than 235 languages', assembled from '170+ countries'. Its physical-AI offering states: 'We deliver egocentric video, pose, gripper state, and sensor fusion streams, synchronized and annotated'. Confirm the requested embodiment, collection setting and action-label QA against your specification. Visit Appen

Strengths

+1M+ contributor crowd from 170+ countries, 235+ languages
+Egocentric video, pose, gripper state and synchronized sensor streams
+Broad modality coverage in one relationship

Limitations

–Confirm schema-specific QA and acceptance criteria
–Confirm the exact egocentric capture and sensor configuration

Best for: Programs needing globally distributed annotation volume where physical AI is one workstream among several.

TELUS Digital

Managed annotation at scale

1M+ AI community, 500+ languages, 3D sensor-fusion annotation

TELUS Digital (formerly TELUS International) fields a '1M+ Diverse global AI Community of annotators and linguists' across '500+ Annotation languages & dialects', reports '2B+ Labels annually', and offers '3D Sensor Fusion Annotation' including 'point cloud segmentation'. Its physical-AI services include egocentric and wrist-mounted video, with collection, annotation and quality assurance. Confirm the required embodiment and sensor synchronization with TELUS. Visit TELUS Digital

Strengths

+Large managed annotation community (1M+) with 500+ languages
+3D sensor fusion and point-cloud segmentation capabilities on the menu
+Enterprise account management and compliance infrastructure

Limitations

–Robotics is one market among many; not physical-AI-first
–Confirm embodiment-specific collection and sensor synchronization

Best for: Enterprises that want a BPO-style managed relationship for large multimodal annotation programs.

Surge AI

Expert human data

Elite expert workforce for RLHF and evaluation data

Surge AI is 'a data labeling platform focused on the richness of AI', built on an elite workforce — 'doctors, lawyers, investment bankers, Fields Medalists, Harvard professors' — with products spanning 'RL Environments and Agents' and 'Rubrics and Verifiers'. Anthropic has publicly described using Surge's RLHF platform. Visit Surge AI

Strengths

+Elite-expert workforce for hard judgment tasks
+RL environments, rubrics and verifiers — built for post-training and evaluation
+Profitable, independent company

Limitations

–Not a robotics-capture or video-annotation-first vendor
–Best fit is expert-domain and evaluation data, not bulk egocentric labeling

Best for: Teams that need expert human-data work for post-training, evaluation and red-teaming adjacent to their robotics program.

Which provider fits your program?

The data does not exist yet: buy capture. Claru (egocentric and workplace video with enrichment), Mecka (first-person human activity plus evaluation), XDOF (teleoperation-native demonstration datasets) or Human Archive (human data plus deployment infrastructure).

The data exists but is unlabeled: buy managed annotation. iMerit for multimodal consistency, Scale AI for foundation-model programs, TELUS Digital for enterprise volume, Appen for global crowd reach, Surge AI for expert-domain and evaluation data.

You need both: dual-source. A capture specialist for first-person data and a managed service for overflow annotation is a common pattern — Claru's robotics labeling companies guide covers that side of the market in detail.

Best egocentric data providers

First-person capture compared in depth

Best VLA training data providers

Action labels and embodiment coverage

Egocentric datasets: academic vs commercial

Open datasets and their licenses side by side

What delivered-program evidence looks like

Ask any provider for numbers at program scale, then check them. Claru's published case studies are the standard to hold:

01
Household humanoid program: 344,000+ accepted hours across 220+ distinct household tasks, from 31,000+ contributors in 32 countries
02
Robot-action captions: 47,251 caption verifications across two passes, 44,555 of them inside 12 days
03
2025 egocentric collection: 386K+ clips from ~500 contributors across wearable, smartphone and activity-specific pipelines
04
RekaDaily-10k: 10,200 processed hours released Apache 2.0 by Reka, collected on the Claru network

Evidence and sources: household humanoid case study · caption verification case study · 2025 collection case study

What to ask before you buy

→Do they collect egocentric video, or only annotate data you provide?
→What enrichment layers do they deliver pre-computed — depth, pose, segmentation?
→Can they produce action-language pairs for VLA training, not just object labels?
→What output formats do they support — RLDS, WebDataset, HDF5, or custom?
→Is the data commercially licensed with full consent documentation?
→What is their collection footprint — how many countries, environments, camera setups?
→Who performs the work — employees, crowd, or experts — and how is QA structured?
→Do they have delivered-program evidence in robotics, not just marketing claims?

Frequently Asked Questions

What does a robotics data provider actually provide?

Three different things share this label: companies that collect new robot-relevant data (Claru, Mecka, XDOF, Human Archive), companies that annotate data you already have (iMerit, Scale AI, Appen, TELUS Digital, Surge AI), and platforms where your own team labels. Most robotics programs eventually need both: capture for data that does not exist yet, and annotation for data that does.

How is robotics training data different from generic AI data?

Robot policies learn from first-person (egocentric) video synchronized with physical actions, and they need enrichment layers — depth, pose, segmentation, optical flow, action labels — that generic annotation pipelines were never designed to produce. A vendor built for text or third-person video cannot fake this; the viewpoint and the label vocabulary have to match the robot's own experience.

Should I buy data or annotate in-house?

Annotate in-house when the data already exists and your schema is stable. Buy collection when the data does not exist — Claru's 2025 egocentric collection delivered 386K+ clips across three capture pipelines that no amount of tooling could have produced from existing stock. Many teams dual-source: a specialist for first-person capture and a managed service for overflow annotation.

What license do I need for commercial robotics training?

Commercial training needs commercially licensed data with consent documentation. Most large open egocentric datasets are research-only: EgoDex ships under CC BY-NC-ND, and EPIC-KITCHENS uses CC BY-NC with commercial use requiring a separate agreement with the University of Bristol. Ego4D's permitted uses are defined by its executed license agreement — the license text governs, not the ability to download.

Do robotics data vendors use employees or crowd workers?

Both models exist. iMerit positions an in-house dedicated workforce (25,000+ domain experts in its Scholars program) against crowd-sourced annotation; Appen runs a global crowd of more than 1 million contributors; Surge AI recruits elite domain experts; Claru recruits and pays a contributor network under program-level consent and QA. Consistency requirements usually decide which model fits.

How much does robotics training data cost?

Request comparable quotes against the same capture specification: tasks, accepted volume, environments, annotation schema, QA thresholds, delivery formats and usage rights. Claru quotes collection programs per specification; confirm each vendor's pricing and included scope directly.

Can I train on open egocentric datasets instead?

Ego4D, EPIC-KITCHENS-100 and EgoDex are useful research reference sets, but their licenses differ. Ego4D's permitted uses are governed by the executed Ego4D license agreement, not an assumed Creative Commons license. EPIC-KITCHENS is CC BY-NC with a separate commercial licensing path; EgoDex is CC BY-NC-ND. Review the terms for your intended use and compare them with commercially licensed capture.

What should I ask a provider before committing?

Ask what is actually delivered (formats, enrichment layers, license), who performed the work, the QA and acceptance process, and for evidence at program scale. Claru's published cases document 44,555 first-pass human-video action-caption verifications in 12 days and a household humanoid program with 344,000+ accepted hours across 220+ distinct tasks from 31,000+ contributors. Keep annotation verifications separate from captured hours when comparing evidence.

Looking for a robotics data specialist?

Claru is built specifically for physical AI and robotics

34,000+ contributors across 70+ countries. 344,000+ hours of household humanoid egocentric video across 220+ distinct tasks — with requested depth, pose, segmentation and action labels. Commercial licensing included.

Talk to the Claru team