Comparison · April 11, 2026 · 14 min read
7 Best Human Motion Data Providers (2026)
Side-by-side review of Field Motion, Claru, Scale AI, Encord, Luel, Ego4D, and Appen across capture, sensor fidelity, and delivery speed.
7 Human Motion Data Providers for Robotics (2026)
Finding useful human motion data for robot policy training is harder than it looks. There are capture services, annotation platforms, open datasets, and marketplaces - and the differences between them matter enormously for what you get. This is every realistic option, evaluated on the dimensions that actually matter for production robotics teams.
We assessed each provider on six dimensions. Not marketing claims - what the product does, what it does not do, and what type of team it fits.
Field Motion deploys trained field operators with calibrated camera rigs and wearable IMU sensors to capture synchronized human motion datasets across real-world environments. Every project starts with a protocol design session with your ML team - the task structure, environment requirements, and annotation taxonomy are built around what your policy needs to learn, not what was convenient to collect.
Sensor package per session: calibrated egocentric video, wearable IMU, monocular depth estimation (Depth Anything V2), 2D and 3D skeletal pose (ViTPose), optical flow (RAFT), and semantic segmentation (SAM) - delivered as synchronized, aligned packages. Annotation is handled by specialists trained specifically on physical AI tasks: grasp taxonomy (Feix GRASP classification), action boundary labeling, affordance labels, and manipulation intent.
Strengths
- Full synchronized sensor stack: video, IMU, depth, pose, flow - aligned, not separate files requiring post-processing merge
- Protocol design included: task structure, environment requirements, and annotation taxonomy scoped collaboratively before capture begins
- Physical AI annotation specialists: grasp taxonomy, action boundaries, affordance labels, manipulation intent - not generic video labeling
- Robotics-native delivery: RLDS, HDF5, WebDataset, Parquet - compatible with LeRobot, OpenVLA, Octo, and custom pipelines
- Pilot datasets scoped and delivered within days of protocol sign-off
- Full commercial licensing with consent documentation on every capture session
Limitations
- Scoped engagements - not a self-serve marketplace or off-the-shelf catalog
- Specialist physical AI focus - not a general-purpose annotation platform for other AI verticals
Claru operates a network of 10,000+ trained contributors capturing egocentric video across 100+ cities worldwide. Their enrichment pipeline adds depth maps, pose estimation, segmentation, optical flow, and AI captions to every clip. Strong at large-scale egocentric video for VLA pretraining and world model training where broad environment diversity matters more than task-specific structure.
Strengths
- Scale: 500K+ enriched egocentric clips, 10,000+ contributors across 100+ cities
- Enrichment pipeline included: depth, pose, segmentation, flow computed per clip
- Strong for VLA pretraining datasets requiring broad real-world diversity
- Delivered in robotics-native formats with commercial licensing
Limitations
- Egocentric video only - no synchronized wearable IMU data alongside video
- Activity capture, not protocol-driven task demonstrations for specific robot policies
- Engagement-based, not self-serve
Scale AI has been running annotation infrastructure since 2016. Their Physical AI Data Engine is designed for robot interaction data, with active learning tools for surfacing rare training scenarios. In early 2026 they launched Scale Labs for model evaluation and safety benchmarking. Proven track record at enterprise scale across autonomous vehicles and robotics programs.
Strengths
- Proven at enterprise scale across autonomous vehicles and major AI labs
- Physical AI Data Engine purpose-built for robot interaction data
- Active learning and AI-assisted pre-labeling reduce annotation cost on large projects
- Scale Labs (2026) adds model evaluation and safety benchmarking
Limitations
- No data capture - you must bring your own motion data
- Generalist infrastructure; robotics is one vertical among many
- Enterprise pricing and sales process - not suitable for quick pilots
- Annotation through Remotasks subsidiary; quality on specialist robotics tasks varies
Encord raised a $60M Series C in early 2026 and serves 300+ physical AI teams. Strong tooling for teams annotating large volumes of existing data: video-native annotation with 6x speed improvements, LiDAR and point cloud support, SAM 2 integration for automated segmentation, RLHF workflows, and embedding-based curation. Well-suited for teams managing large internal data pipelines.
Strengths
- Best-in-class annotation tooling for teams with existing data pipelines
- SAM 2 integration, AI-assisted labeling, embedding-based curation
- Native LiDAR, video, and sensor fusion support - broad modality coverage
- Model evaluation and RLHF workflows alongside annotation in one platform
Limitations
- Does not capture data - teams bring their own video and sensor data
- SaaS subscription model - you operate the tools, not receive delivered data
- Enterprise pricing at scale
Luel (YC W26) is a two-sided marketplace connecting AI teams with 3M+ vetted contributors. Off-the-shelf datasets with same-day delivery. Strong compliance infrastructure and a growing content library. Early stage but fast-moving. Useful for teams that need licensed video quickly and have in-house enrichment pipelines.
Strengths
- Fastest time-to-data for off-the-shelf datasets: same-day delivery available
- 3M+ contributor network for custom campaigns at scale
- Strong compliance and rights documentation on every clip
Limitations
- No deep enrichment pipeline - raw or lightly processed video only
- Marketplace model means quality varies across contributors
- Not designed for structured protocol-driven task demonstrations
- Limited production track record at this stage
Ego4D is the largest open egocentric video dataset: 3,670 hours from 931 camera wearers across 74 locations in 9 countries. Ego-Exo4D pairs egocentric with exocentric views using Project Aria glasses. Widely used as a pretraining baseline in academic robotics research. Free for research use under the Ego4D License Agreement.
Strengths
- Largest open egocentric dataset by a significant margin - 3,670 hours
- Exceptional geographic and activity diversity across 74 locations, 9 countries
- Free for research - accessible to teams at any funding stage
- Established benchmark suite with active research community
Limitations
- Academic license - commercial use may be restricted and requires review
- No robot action labels; human data only, requiring retargeting
- No enrichment layers pre-computed; teams run depth/pose/segmentation in-house
- Fixed dataset - cannot commission new data to your specifications
- 48-hour license approval process
Appen has 30 years in AI data and 1M+ contributors across 170 countries. They contributed to the original Ego4D dataset and have expanded into LiDAR annotation and robot demonstration data. Geographic diversity is a genuine strength. The business has faced financial headwinds in recent years.
Strengths
- Massive geographic diversity: 1M+ contributors across 170 countries
- End-to-end pipeline: collection, annotation, and validation in one vendor
- Enterprise compliance and PII handling for regulated industries
- Contributed to Ego4D - proven experience with egocentric video at research scale
Limitations
- Generalist platform - physical AI is not the core focus
- Crowd annotation quality for specialist robotics tasks is inconsistent
- Significant financial losses in recent years may affect service quality
- Heavy onboarding built for large enterprise contracts - slow to start
Quick comparison
| Provider | Type | Captures data | Sensor stack | Physical AI annotation | Commercial license |
|---|---|---|---|---|---|
| Field Motion | Service | Yes | Full (video + IMU + depth + pose) | Yes - specialist | Yes |
| Claru | Service | Yes | Partial (video + computed) | Yes - general | Yes |
| Scale AI | Platform | No | None | Partial | Yes |
| Encord | Platform | No | None | Tooling only | N/A |
| Luel | Marketplace | Yes | Video only | No | Yes |
| Ego4D | Open dataset | Fixed | Video + some multimodal | No | Academic |
| Appen | Crowd | Limited | Video only | General only | Yes |
How to choose
You need task demonstrations captured to your protocol - Field Motion. Protocol design, field deployment, synchronized sensors, specialist annotation, robotics-native delivery. End-to-end from brief to dataset.
You need large-scale egocentric video for VLA pretraining - Claru for commercial use with enrichment. Ego4D as a free research baseline you enrich in-house.
You have data and need annotation tooling or infrastructure - Encord for teams managing their own annotation ops. Scale AI for enterprise annotation at volume.
You need licensed video quickly with in-house enrichment - Luel. Same-day off-the-shelf delivery with commercial rights.
Frequently asked questions
What is the best human motion data provider for robotics?
It depends on your need. Field Motion is best for task demonstrations captured to a specific protocol with synchronized sensor data and specialist physical AI annotation. Claru is best for large-scale VLA pretraining egocentric video. Scale AI and Encord are best for teams with existing data that need annotation infrastructure. Ego4D is best for academic research baselines (free, academic license). Luel is best for licensed video with fast delivery and in-house enrichment.
What is the difference between a motion data provider and an annotation platform?
A motion data provider like Field Motion or Claru captures new data and delivers it training-ready. An annotation platform like Encord or Labelbox provides software tools for teams to label data they already have. For teams that lack physical demonstration data, a capture service is needed first. For teams with raw video needing labels, a platform or managed annotation service may be sufficient.
Does Encord capture robot training data?
No. Encord is a software platform - teams bring their own data. Encord provides tools to manage, curate, annotate, and evaluate it. It does not operate a field capture network or generate demonstrations. If you need motion data captured, Field Motion and Claru are the relevant options.
Can I use Ego4D data commercially?
Ego4D requires license approval and restricts commercial use for some components. Teams building commercial products should review the Ego4D License Agreement. For unrestricted commercial use, Field Motion and Claru provide motion data with full commercial licensing and consent documentation included.
References
- [1] Chang et al. (2024). EgoMimic: Scaling Imitation Learning via Egocentric Video. arxiv.org/abs/2410.24221
- [2] Feix et al. (2016). The GRASP Taxonomy of Human Grasp Types. IEEE Trans. Human-Machine Systems. doi.org/10.1109/THMS.2015.2481603
- [3] Grauman et al. (2022). Ego4D: Around the World in 3,000 Hours of Egocentric Video. CVPR 2022. arxiv.org/abs/2110.07058
- [4] Encord Series C (2026). Encord raises $60M to build the AI data platform for physical AI. encord.com
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