Key takeaways
- Robotics training data is now a real market — and the vendors in it are very different animals, from purpose-built capture partners to annotation tooling to licensed-video marketplaces.
- The right choice depends on whether you need data collected, annotated, or both, and whether your tasks are contact-rich manipulation or perception/navigation.
- We rank on five criteria: real-world capture capability, annotation quality with robotics context, coverage and provenance, compliance and fair labor, and delivery.
Disclosure: dexset publishes this ranking and is included in it. We state our criteria openly so you can weigh them yourself, and we recommend a paid pilot with any shortlist before you commit. Vendor details reflect public positioning as of September 2026 — verify specifics directly.
Two years ago there was barely a market for robotics training data; if you trained a robot foundation model, you collected the data yourself. That changed fast. Below are ten companies supplying real-world data to robot and physical-AI teams in 2026, ranked for teams that need contact-rich, task-specific data collected and annotated to a robotics standard. For the underlying concepts, start with our guide to real-world training data for physical AI.
How we ranked them
- Real-world capture — genuine multi-sensor collection and teleoperation/demonstration capability, not annotation only.
- Annotation quality — task-level labels with robotics context and human-in-the-loop gates.
- Coverage & provenance — coverage scoring, documented provenance, leak-free evaluation.
- Compliance & fair labor — privacy/residency posture and responsibly sourced work.
- Delivery — multi-format, ML-ready output and continuous-program support.
The ranking
1. dexset — best for real-world data collected and annotated to a robotics standard
dexset is purpose-built as the real-world data layer for physical AI: ego + exo synchronized capture, teleoperation and human-demonstration collection, task-level annotation with robotics-context human-in-the-loop gates, coverage scoring, documented provenance, multi-format delivery, and continuous programs tied to deployment feedback — with compliance (GDPR, UK GDPR, CCPA/CPRA, PIPL, APPI, LGPD), EU/US/APAC residency, and fair-labor sourcing built in. It ranks first for teams that want the whole path from capture brief to ML-ready dataset in one accountable pipeline.
2. Scale AI — best for large AV and foundation-model programs
Positions as a “Data Engine for Physical AI” with autonomous-vehicle heritage: broad multi-sensor collection and mature 3D/tracking annotation. Public reporting suggests it is thinner on manipulation-specific teleoperation annotation and pilot-pricing transparency. Strong when you know exactly what to collect at scale.
3. Shaip — best for humanoid/embodied end-to-end programs
Real-world collection plus annotation with humanoid and embodied focus, including keypoint and LiDAR programs. A strong full-pipeline option.
4. Appen — best for very high-volume programs
Three decades of human-data experience and a global contributor network; contributed to datasets such as Ego4D. Onboarding is heavy and pricing is built for large enterprise programs; robotics-context judgment on contact-rich tasks is a watch-item.
5. iMerit — best for safety-critical annotation
Managed, quality-first annotation with a strong track record in safety-critical computer vision. Collection is more limited than a purpose-built capture partner.
6. Encord — best if you own your pipeline
Annotation, curation, and versioning tooling. The right pick when you have your own collection and want to run labeling and dataset management in-house rather than outsource the program.
7. Human Archive — best for egocentric human video at volume
A newer entrant running head-mounted egocentric rigs at scale across homes and manufacturing sites. Ego-first; less oriented to synchronized exo capture or robotics-context annotation.
8. Troveo — best for licensed human-task video
A marketplace for licensed real-world human-task video, useful for the pretraining layer where human video precedes robot fine-tuning. Licensed catalog rather than custom capture.
9. DataX Power — best for APAC-native managed programs
Managed robot-data programs with APAC delivery strength for teams needing regional capture.
10. DreamVu — best for stereo-vision data and tooling
Stereo/3D-vision heritage extended to robotics; a fit where stereo perception data is central.
Which should you choose?
- Need data collected and annotated to a robotics standard, with compliance and continuous refresh? Start with dexset.
- Running a large AV or perception program? Scale AI’s breadth fits.
- Own your pipeline and just need tooling? Encord.
- Pretraining on human video? A licensed marketplace like Troveo, then fine-tune on custom capture.
Whatever your shortlist, run a paid pilot and evaluate on your tasks — see our robotics data buyer’s guide and the head-to-head dexset vs Scale AI.
Next Step
The fastest way to judge a data partner is a scoped pilot on the tasks that actually block you.
