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
COCO, CVAT, YOLO, custom JSON, ROS bag with annotation overlays, and RLDS for trajectory data. If you use an internal format, share the schema and we will build a converter.
Our baseline is 99.4% label accuracy across active programs. We achieve this through multi-pass review, consensus labeling on ambiguous frames, and a dedicated QA team that audits every batch before delivery.
Every label goes through automated validation checks, a human review pass, and a final QA audit. Batches that do not meet your agreed accuracy threshold are re-labeled at no additional cost.
Yes. Send us your label taxonomy, class definitions, and any edge-case guidance before the program starts. We build that into operator training and validation tooling.
Ambiguous frames are flagged in the delivery manifest with a confidence score. You choose whether to include them in training, discard them, or send them back for adjudication.
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
Warehouse robots underperform in production not because of the model, but because training data missed the edge cases that matter.
Sub-millimeter precision, HIPAA compliance, and credentialed operators — surgical robot data has requirements general robotics programs cannot meet.
Robotics data quality is not a review meeting. At production scale it is automated validation, per-operator metrics, and same-day feedback loops.
Robot annotation is not image labeling with a new name. Temporal structure and task semantics demand distinct tooling and annotator qualification.
Simulation offers unlimited training data at zero cost. The sim-to-real gap is a structural problem, not a rendering one.
Language models scaled on internet data. Embodied AI must build its data from the physical world — and that changes everything.
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.
