Home / Data services / Long-tail and edge-case capture
Targeted collection for failure modes identified from your production model logs.
Long-tail capture is the targeted collection of demonstration data for the specific failure modes your production model struggles with — transparent objects, wet surfaces, occluded reaches, deformable materials, and other scenarios that rarely appear in general-purpose datasets.
Staging rare scenarios in-house is expensive and slow. We maintain prop libraries, environment rigs, and trained operators who specialize in the hard cases.
Why outsource edge-case capture?
Your lab is optimized for the common case. We maintain dedicated environments for the uncommon ones — wet benches, transparent object libraries, clutter generators.
100+ edge cases captured per week.
7 days from log analysis to training data.
4x improvement on targeted failures.
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 outputs that turn long-tail from a discovery phase into an iteration loop.
Classified failure modes from your production logs, ranked by frequency and severity.
Capture plans designed to hit each failure class. Spec-locked before collection.
Scenes designed to break your current policy. Useful for safety and robustness.
Post-injection tracking. Each captured failure is monitored for recurrence.
The seven-day loop that turns production failures into resolved cases.
Analyze your production logs. Cluster failures. Identify patterns and frequencies.
Build a collection plan for each failure class. Acceptance criteria defined.
Targeted teleop or sensor capture against the spec. Daily QA review.
Add to your training set. Track post-deployment recurrence rate.
Log analyzers and capture rigs working as one loop.
Production logs
Reusable cases
Targeted teleop
Failure binning
Post-injection
Your pipeline
FAQ
We work with your team to identify scenarios where your current policy fails, degrades, or has never been tested. Edge cases are defined relative to your deployment distribution, not an abstract standard.
Through structured variation of environmental parameters — lighting, object placement, surface texture, human presence, and task interruption — combined with adversarial prompting of operators to find natural failure modes.
Long-tail programs typically run in smaller batches — 50 to 500 scenarios per iteration — because each scenario requires more setup than standard capture. The value is precision, not volume.
Long-tail capture is priced per scenario rather than per hour or per trajectory, reflecting the higher setup cost per data point. We provide a fixed price per scenario type in the SOW.
From the blog
Humanoid Robot Training Data: How Much Do You Actually Need?When long-tail and edge-case data becomes the bottleneck.
From the blog
Warehouse Picking Robots: What Your Training Data Strategy Is MissingDeformable items and edge cases in warehouse robotics.
Send us your production logs. We classify, scope a capture plan, and start the cycle.
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.
Bounding boxes, segmentation, action labels.
Domain-randomized scenes and sim transfers.
Held-out test sets and success-rate scoring.
