Surgical data collection captures procedure-grade demonstrations from clinician operators on research-grade surgical robots. Every trajectory is logged with sub-millimeter tool-tip pose, grip force, and stereo video — under HIPAA-compliant protocols with IRB approval and full de-identification.
Why outsource surgical data?
Clinician time is the bottleneck. We coordinate operators, handle IRB compliance, and deliver de-identified datasets so your ML team focuses on models, not protocols.
3 surgical platforms.
6 procedure types.
HIPAA compliant end-to-end.
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 streams produced by clinician operators, audited at sub-frame precision.
Surgeon-quality trajectories with tool-tip pose, sub-millimeter accuracy.
Force-torque profiles for contact-sensitive tasks where tissue trauma matters.
Operators with surgical training, not generic teleoperators. Bar matches OR standards.
Same procedure across body habitus, anatomy variation, and complication patterns.
A four-stage pipeline designed for IRB-compliant capture and FDA-grade audit trails.
Define the procedure, the success criteria, and the variation matrix with your clinical team.
Operators with surgical training matched to procedure type. Credentials verified.
Sub-millisecond synchronized capture in a HIPAA-bound environment. Audit trail per session.
Per-procedure quality review by a senior clinician. Failure cases flagged, not silently shipped.
Production and research surgical robots from across the field.
Production
Production
Production
Production
Production
Production
Four verticals. One data partner.
Whole-body trajectories across 24 platforms.
Long-horizon tasks in real environments.
Pick-pack-place across real SKU diversity.
Remote operator-driven data collection.
In-person task demos for imitation learning.
Held-out test sets and success-rate scoring.
FAQ
Operators working in or around clinical environments are trained in infection control, patient privacy, and relevant local health regulations. Our program leads carry applicable certifications for the jurisdiction.
We do not collect patient data. Our programs capture robot motion and sensor data in clinical environments — not patient records, clinical observations, or any individually identifiable health information.
HIPAA in the US, GDPR in Europe, and equivalent frameworks in other jurisdictions. Our data handling practices are designed to comply with the most stringent applicable framework by default.
Any incidental capture of clinical staff, patients, or identifiable clinical information is reviewed and redacted before delivery. We can provide a documented anonymisation protocol on request.
Yes, with appropriate access coordination, credentialing, and infection control compliance. We have experience working within active hospital departments and surgical simulation centers.
From the blog
Training Data for Surgical Robots: HIPAA, Precision, and ScaleCompliance requirements and data standards for surgical robot training.
From the blog
The QA Pipeline Every Robotics Data Team NeedsQA standards for procedure-grade teleoperation data.
Tell us the platform and the procedure. We come back with a clinician roster and IRB plan.
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.
