Home / Data services / Annotation and labeling
Action segmentation, affordance masks, language captions, reward signals — at the bar your model deserves.
Annotation and labeling is the process of adding structured metadata to raw robotics data — action boundaries, affordance masks, natural-language captions, and reward signals — so your model knows what happened, where, and why it mattered.
Labeling robotics data requires domain expertise, not just clicking boxes. Our annotators are trained on manipulation, locomotion, and navigation tasks.
Why outsource labeling?
Your engineers should be iterating on architectures, not drawing masks. We scale annotation teams up and down to match your training cycles.
1,200 labels/hr per trained annotator.
48-hour turnaround on standard batches.
14 modalities supported out of the box.
Where we collect
41+ delivery centers across 12 countries. Every program runs from a Roborax hub near your target time zone.
Asia Pacific
India · Philippines
Americas
USA · Canada · Colombia · Jamaica · El Salvador · Belize
EMEA
UK · Albania · Kosovo · Morocco
Four label types that bridge raw capture to a training-ready dataset.
Frame-level action labels with start, end, and class for every clip.
Pixel-level masks of graspable, pushable, and contact regions.
Free-form and templated natural-language descriptions per scene.
Dense or sparse reward per timestep for RL and IRL pipelines.
Four stages that lock the bar before billable work and verify it after.
Acceptance criteria locked with your team. Edge cases documented in writing.
Operators pass a gold-set bar before producing billable labels.
Production labeling with daily QA review and same-day rejections.
Held-out 5% sample re-reviewed by seniors. Accuracy report per batch.
Use ours, use yours, or use the open-source standard.
OSS, self-host
Enterprise SaaS
Enterprise SaaS
Multi-modal
Lightweight
Your pipeline
FAQ
From the blog
Robot Data Annotation: A Practical Guide for ML TeamsRubrics, workflows, and QA standards for robot annotation programs.
From the blog
The QA Pipeline Every Robotics Data Team NeedsHow to build quality assurance into your annotation pipeline.
Send us a sample batch and the spec. We come back with a calibrated quote in three days.
FROM THE FIELD
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Surgical robot training data has requirements that no general-purpose robotics data program is built to meet out of the box. Sub-millimeter precision, HIPAA compliance, and
A robotics data quality assurance pipeline is not a checklist or a review meeting. At production scale, robotics data quality requires automated validation, per-operator metrics,
Robot data annotation is not image labeling with a different name. The temporal structure of robot trajectories, the grounding in physical task semantics, and the
Sim-to-real robot training with synthetic data is one of the most powerful techniques in embodied AI — and one of the most misunderstood. The gap
The embodied AI training data problem is structurally different from the language model data problem. Language models learned from the internet. Embodied AI must learn
Seven services. One synchronized pipeline.
VR and leader-follower robot control logging.
In-person task demos for imitation learning.
RGB-D, LiDAR, force, and tactile streams.
Domain-randomized scenes and sim transfers.
Held-out test sets and success-rate scoring.
Rare scenarios your policy will face in production.
