A robotics startup (illustrative) · Robotics & Embodied AI · Multimodal (RGB-D, LIDAR, IMU)
Physical AI data-annotation engagement for a robotics team
A spatial-annotation SERVICE engagement: 3D boxes, point-cloud segmentation and 6-DoF pose on multi-sensor capture the team already had.
By Cognegica Quality & Standards · QA & annotation-standards team
3D box & pose IAA ≥ 0.85
Languages: —
Illustrative scenario. This case study describes a representative methodology rather than a specific client engagement.
Challenge
A robotics team had collected hours of multi-sensor data — RGB-D, LIDAR and IMU — but lacked the in-house capacity to label it to a consistent, measured quality bar. Generic 2D annotation vendors couldn't handle sensor-fusion or 6-DoF pose, and inconsistent labels were poisoning their perception models.
Approach
We ran this as a data-annotation service engagement (not research, and with no partner named):
- 3D bounding boxes, point-cloud segmentation, 6-DoF pose and frame-accurate event labeling on the team's existing captures.
- Calibrated guidelines piloted on a sample set; two-pass QA with adjudication and spatial-agreement metrics reported per batch.
- An India-residency option for sensitive captures.
Outcome
Consistent spatial ground truth with inter-annotator agreement held at ≥ 0.85 on 3D boxes and pose, delivered as versioned drops with audit logs — letting the team retrain perception on labels they could trust.
Representative engagement illustrating our Physical AI annotation service and quality bar. Physical AI is a data service, not a research program.
How this maps to what we do
The services and data behind this engagement
This outcome was delivered with the same rights-cleared, documented services and datasets you can engage today.
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Physical AI Data Annotation
3D boxes, point-cloud segmentation and 6-DoF pose — a spatial-annotation service.
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Physical AI Data Collection
Multi-sensor field capture across real environments — a collection service.
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Robotics & Embodied AI
How our data services support embodied-AI teams.
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Annotate the sensor data you've already collected.
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Written by
Cognegica Quality & Standards
QA & annotation-standards team
Cognegica Quality & Standards is the internal team that defines and enforces our annotation guidelines, multi-layer QA, native-linguist review and inter-annotator agreement reporting. This is an editable team identity — a named reviewer with a public profile can be assigned to it later in the admin.