Last updated: April 2026
Best VLA Training Data Providers in 2026
Vision-language-action models need a specific kind of data that almost no general-purpose annotation company can produce. This guide breaks down every viable vendor for sourcing VLA training data in 2026 — covering egocentric video capture, action labeling, enrichment pipelines, and commercial licensing.
Why VLA Training Data Is Uniquely Hard to Source
Training a vision-language-action model requires three things that almost never come packaged together: first-person egocentric video, natural language task instructions, and timestamped action labels that describe what the demonstrator did at each frame. You cannot source this from existing datasets scraped from the internet. Every training example requires a physical person performing a real task while wearing a camera — and then a human annotator pairing that footage with language instructions and action boundaries.
Most annotation companies — even large ones — were built for third-person video or static image annotation. They annotate bounding boxes, object classes, and segmentation masks well. But producing egocentric video with action-language pairs is a fundamentally different pipeline. It requires physical collection infrastructure, egocentric-specific annotation tooling, and enrichment layers (depth, pose, segmentation) that transform raw footage into data a VLA model can actually train on.
The providers below represent the realistic options for sourcing this data in 2026. We evaluated each on egocentric collection capability, enrichment depth, action label structure, format compatibility, and commercial licensing.
Direct Answer
The best VLA training data providers as of 2026
- Claru AI — Purpose-built egocentric video with pre-computed enrichment layers and VLA-native format delivery. Best overall for physical AI and VLA teams.
- Scale AI — Enterprise annotation infrastructure with a Physical AI Data Engine. Best for large programs that already have robot demonstration data.
- iMerit — Managed data operations with an expanding physical AI practice. Combines collection and annotation with a salaried (not crowd-sourced) workforce.
- TELUS Digital — Large contributor network for volume annotation. Generalist, not specialized in VLA, but viable for high-volume label tasks.
- Appen — Legacy annotation platform with egocentric experience via Ego4D contributions. Broad geographic reach, not specialized in physical AI.
Provider Breakdowns
Claru AI
Purpose-built egocentric data for physical AI and VLA models
Claru AI captures, enriches, and delivers egocentric video datasets purpose-built for VLA model training. The network spans 10,000+ trained collectors across 100+ cities on 5 continents, with cameras worn during real tasks in kitchens, warehouses, farms, construction sites, restaurants, and labs. Every clip is enriched by default with depth maps (Depth Anything V2), human pose estimation (ViTPose), semantic segmentation (SAM 2), optical flow, and AI-generated captions. Expert annotators add action boundary labels, object affordance annotations, grasp type classifications, and natural language instruction pairs — the exact structure VLA models need at training time.
Strengths
- ✓500K+ enriched egocentric clips pre-loaded with 5+ annotation layers — not raw video
- ✓Egocentric captures across 20+ environment categories matching real robot deployment settings
- ✓Delivers in VLA-native formats: RLDS, WebDataset, HDF5, Parquet — compatible with OpenVLA, Octo, Pi-0, LeRobot
- ✓Custom collection campaigns scoped to brief, with brief-to-delivery measured in days
- ✓Full commercial licensing with consent documentation on every clip
- ✓Can co-train on human egocentric data and robot demonstrations in a unified dataset
Limitations
- —Not a self-serve marketplace — collection is collaborative and scoped
- —Focused on physical AI; not a general-purpose annotation platform
Scale AI
Enterprise annotation infrastructure with a Physical AI Data Engine
Scale AI has operated annotation infrastructure since 2016 and built its Physical AI Data Engine specifically for robot learning workloads. The platform combines AI-assisted pre-labeling, active learning to surface hard examples, and a managed workforce through Scale's Remotasks subsidiary. Scale can handle LiDAR point clouds, multi-camera sensor fusion, robot teleoperation trajectories, and video annotation. Their enterprise offering includes SLA-backed delivery, custom ontologies, and integrations with major ML frameworks.
Strengths
- ✓Physical AI Data Engine provides structure specifically for robot interaction data
- ✓Active learning surfaces rare and high-value training scenarios automatically
- ✓Enterprise security, compliance, and SLA guarantees for large programs
- ✓Proven at scale across autonomous vehicle and major AI lab customers
- ✓Scale Labs (2026) adds model evaluation and safety benchmarking
Limitations
- —No egocentric video collection network — annotates data you bring, does not capture it
- —Robotics is one vertical among many; not a specialist in VLA or physical AI
- —Enterprise pricing and sales-driven onboarding; not suited for small teams
- —Annotation quality for specialized manipulation tasks depends heavily on project management
iMerit
Managed data operations with an expanding physical AI practice
iMerit provides managed data operations for AI — annotation, collection, and QA services delivered through a hybrid workforce of salaried employees and trained contractors. The company has built a physical AI practice covering egocentric video annotation, LiDAR labeling, sensor fusion, and robot demonstration data. They offer both workforce-as-a-service for teams that want to outsource annotation and a managed platform for teams that want tools plus operators. iMerit has worked with robotics companies on grasping, manipulation, and navigation datasets.
Strengths
- ✓Salaried employee model (not crowd-sourcing) produces more consistent annotation quality
- ✓Physical AI practice with experience on manipulation and grasping datasets
- ✓Egocentric video collection capability alongside annotation — a rarer combination
- ✓Flexible engagement: workforce-as-a-service, platform, or hybrid
- ✓Strong data governance and privacy controls for enterprise customers
Limitations
- —Smaller scale than Scale AI or Appen for very high-volume programs
- —Less specialized than Claru AI in egocentric-first collection and enrichment
- —Collection geography is narrower than providers with globally distributed networks
- —VLA-specific annotation structures (language-action pairs) require custom scoping
TELUS Digital
General AI training data at enterprise scale
TELUS Digital (formerly Lionbridge AI) is one of the largest AI data services companies globally, with a contributor network exceeding 1 million people across 200+ countries. Their services span data collection, annotation, localization, and AI model evaluation. TELUS Digital produces a buyer's guide for physical AI training data and offers video annotation, image classification, and audio transcription at volume. Their scale makes them viable for programs that need massive geographic diversity or multilingual coverage alongside visual data.
Strengths
- ✓1M+ contributor network across 200+ countries for global coverage
- ✓Competitive pricing at high volume due to scale efficiencies
- ✓Broad modality coverage: text, audio, image, video, and structured data in one vendor
- ✓Strong enterprise account management and compliance infrastructure
Limitations
- —Generalist platform — not specialized in VLA, egocentric video, or physical AI
- —No egocentric video collection infrastructure built for robotics use cases
- —Does not deliver pre-enriched datasets with depth, pose, or segmentation layers
- —VLA-native format delivery requires custom engineering work
- —Better suited for volume annotation tasks than specialized manipulation data
Appen
Legacy annotation platform with broad contributor reach
Appen has operated in AI training data since 1996 and built one of the world's largest contributor networks: 1M+ contributors across 170+ countries. Their ADAP platform combines crowd-sourced annotation with AI-assisted labeling, quality monitoring, and domain expert access. Appen contributed to the original Ego4D dataset and has annotated robotics and autonomous vehicle data. Their catalog includes LiDAR annotation, multi-camera sensor fusion, and action recognition labels for video.
Strengths
- ✓1M+ contributors in 170+ countries for exceptional geographic diversity
- ✓Contributed to Ego4D — direct experience with egocentric video at academic scale
- ✓End-to-end pipeline: collection, annotation, validation, and evaluation
- ✓Multi-modal annotation in one workflow: video, LiDAR, audio, text
Limitations
- —Not specialized for physical AI — egocentric video is one of many data types
- —Crowd-sourced annotation for specialized manipulation tasks has known quality risks
- —Significant financial challenges in recent years may affect service investment
- —Does not deliver VLA-ready datasets with pre-computed enrichment layers
- —Legacy enterprise model with heavy onboarding for smaller programs
What to Look for When Choosing a VLA Data Provider
Six criteria that separate VLA-capable vendors from general-purpose annotation companies.
Egocentric Collection Capability
Can the vendor actually capture first-person video, or do they only annotate data you bring? For VLA training, this matters — you cannot train a robot policy on third-person video.
Enrichment Depth
Do they deliver pre-computed depth maps, pose estimates, and segmentation masks, or raw video only? Enrichment pipelines take months to build in-house.
Action Label Structure
Can they produce language-action pairs — the actual training signal for VLA models — or only object detection and classification labels?
Environment Diversity
Does the vendor's collection footprint cover the environments where your robot will operate? Diversity in clutter, lighting, and object arrangement is what drives generalization.
Format Compatibility
Does the output arrive in RLDS, WebDataset, or HDF5 — formats your training pipeline already ingests — or does it require significant format conversion work?
Commercial Licensing
Is every clip commercially licensed with full contributor consent documentation? Academic-licensed data (Ego4D) creates downstream IP risk for production products.
Frequently Asked Questions
What is VLA training data?
VLA training data is the collection of video observations, language instructions, and action labels used to train vision-language-action models. Each training example pairs an egocentric video observation with a natural language instruction ('pick up the red mug') and a sequence of low-level robot actions (joint positions, gripper states) that complete the task. The highest-quality VLA training data comes from human demonstrations captured in first-person view, enriched with depth maps, pose estimation, and semantic annotations.
Why is VLA training data hard to source?
VLA training data is hard to source for three reasons. First, it cannot be scraped — every example requires a physical person (or robot) performing a real task in a real environment. Second, useful VLA data requires egocentric viewpoints (first-person perspective) that match the camera geometry of the target robot's sensors. Third, raw video is not sufficient — each clip must be paired with language labels and action annotations before it trains a VLA model. This combination of physical collection, egocentric perspective, and multi-layer annotation makes VLA training data more expensive and harder to scale than text or general image data.
How much VLA training data does a model need?
The data requirement varies significantly by task complexity and desired generalization. Specialized single-task policies can achieve strong performance with 100–1,000 demonstrations. Generalist VLA models like OpenVLA and Pi-0 require tens to hundreds of thousands of diverse demonstrations to generalize across environments and objects. NVIDIA's research on EgoScale demonstrated that pretraining on 20,000+ hours of egocentric human video before fine-tuning on robot data substantially improves downstream performance, suggesting the right question is not just 'how many robot demos' but 'how much egocentric pretraining data.'
What environments should VLA training data cover?
The best VLA training datasets cover the environments where the target robot will operate, plus substantial diversity around them. For household robots: kitchens, bathrooms, living rooms across different home styles, lighting conditions, and object arrangements. For industrial robots: warehouse aisles, workbenches, conveyor systems, outdoor loading docks. For service robots: restaurant kitchens, retail floors, hospital corridors. Claru AI collects egocentric video across 20+ distinct environment categories including farms, restaurants, construction sites, labs, and retail — environments that reflect real-world deployment conditions for physical AI systems.
What is the difference between VLA data and general robotics annotation?
General robotics annotation typically means labeling third-person video or LiDAR point clouds with bounding boxes, segmentation masks, or object classes. VLA training data requires a fundamentally different structure: egocentric (first-person) video paired with language instructions and action sequences. The viewpoint must match the robot's own camera perspective. The labels must capture the task intent (language) and the execution (low-level actions). Most legacy annotation providers built for autonomous vehicles or general computer vision cannot produce this structure without significant retooling.
Ready to Source VLA Training Data?
Claru AI delivers egocentric video built for VLA training
10,000+ collectors. 100+ cities. 5 continents. Pre-enriched with depth, pose, segmentation, and action labels. Delivered in RLDS, WebDataset, or HDF5 — ready to plug into your training pipeline.
Talk to the Claru team