Physical AI Annotation
Safety & Anomaly Annotation for Robots Working Around People
First-person video is shaky and full of hidden objects. Shaip’s experts collect and label it, with consent, so robots can learn from how people work.
What is safety and anomaly annotation in robotics?
Safety and anomaly annotation is the labeling of events that deviate from expected, safe operation — out-of-place objects, people entering robot work zones, near-collisions and malfunctions — together with the response a robot should take.
In data science, an anomaly is a pattern that does not match expected behaviour. For robots that work near people, human-proximity labels mark safe, caution and danger zones, so the model learns when to slow down, stop or move away.
Types of safety & anomaly annotation we deliver
Anomaly labels
Objects out of place, humans in work zones, malfunctions.
Collision-risk labels
Path intersections with severity and avoidance action.
Human-proximity labels
Safe / caution / danger zones, mappable to ISO 10218:2025 (incl. former ISO/TS 15066).
Recovery-behaviour labels
Stop, retreat, ask for help, retry — fault handling.
How Shaip delivers safety & anomaly annotation — from pilot to production
A clear, QA-gated workflow from scope to training-ready delivery.
- Scoping. We map your requirements, taxonomy, label schema and edge cases, and design the annotation workflow around your data.
- Pilot. We run a pilot project sized to your requirement, to calibrate guidelines and align on quality before scaling.
- Expert annotation. Experienced, domain-matched annotators label your data to the agreed schema.
- Review & QA. Every batch goes through structured, multi-layer expert review before delivery.
- Delivery. Training-ready data is delivered in your required format, with iteration as the project needs.
Why robotics & Physical AI teams choose Shaip for safety & anomaly annotation
One accountable partner for the full Physical AI data stack — built for deployment-grade quality, not crowd output.
End-to-End, One Engagement
From collection to annotation, synthetic generation, RLHF, and safety benchmarks — under one SLA, one PM, one accountable partner.
Global Real-World Reach
Demonstrations, egocentric activity, and edge cases captured across 60+ countries and 10 environment types — managed, not crowdsourced.
Multi-Modal Depth
Vision, LiDAR, IMU, language, action, and workflow context — structured for how Physical AI actually trains, evaluates, and ships.
Credentialed Domain Experts
Vetted human-in-the-loop workforce. Domain-matched annotators with structured QA — built for deployment-grade accuracy, not crowd quality.
Sim-to-Real Ready
Real-world capture, synthetic generation, real2sim pipelines, domain randomization, and paired benchmarks to close the sim-to-real gap.
Compliance Built In
GDPR, HIPAA-ready, SOC 2 Type II, ISO 27001, ISO 9001. Signed consent, auditable provenance, and data-handling agreements ready for legal review.
Success Stories
Robot Imitation Learning — Humanoid Task Demonstrations
High-fidelity human task demonstrations annotated at scale for humanoid robot imitation learning.
Problem: Capture and annotate high-fidelity human demonstrations across diverse real tasks for imitation learning.
Solution: 50,000+ annotated frames — warehouse, assembly and kitchen — with 42-keypoint skeleton labels. [VERIFY]
Result: Faster robot-policy convergence and stronger real-world task generalisation.
Advancing Embodied AI with Real-World Egocentric Video Data
Large-scale first-person video of everyday tasks, collected and annotated for activity-recognition and robotics models.
Problem: Capture natural, non-staged first-person footage — hands and work area in view — with consent and fast QA at scale.
Solution: Head-mounted smartphone capture via a dedicated app, with location verification, one-day review and hand-object visibility checks.
Result: 30,000 hours collected · 24,000+ hours annotated and training-ready, across the US and Latin America.
Security & Compliance
Get a Physical AI Data Quote
Tell us your data type and volume. We scope a pilot you evaluate against your own benchmarks, then scale it with experienced annotators and multi-layer QA.
Contact UsFrequently Asked Questions (FAQ)
1. What does safety and anomaly annotation include?
Anomaly labels (objects out of place, people in work zones, malfunctions), collision-risk labels with severity and avoidance action, human-proximity zones and recovery-behaviour labels.
2. Which safety standards can your labels align with?
Human-proximity zones can be mapped to ISO 10218:2025, which now includes the former ISO/TS 15066 collaborative-robot guidance, or to your own safety team’s zone definitions. The data supports — it does not replace — your formal risk assessment.
3. How do you find rare edge cases?
Dedicated, separately trained safety annotators review footage with an adversarial mindset — looking for what could go wrong, not just what is present — and edge cases can be captured across 60+ countries and 10 environment types.
4. Do you label how the robot should respond?
Yes. Recovery-behaviour labels mark the correct response — stop, retreat, ask for help or retry — so models learn fault handling, not just fault detection.
5. How is footage of workers and bystanders protected?
Capture runs on signed consent, de-identification is available, and every dataset ships with auditable provenance under SOC 2 Type II, ISO 27001, HIPAA and GDPR controls, with an NDA signed on day one.
6. How do we start a project with Shaip?
Share your data type, volume and target labels. We scope the object-labeling guidelines and edge cases with your team, run a pilot sized to your requirement that you evaluate against your own benchmarks, then scale with multi-layer QA.
