Physical AI Annotation
Egocentric Video Annotation for Embodied AI and Robot Learning
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 egocentric video annotation?
Egocentric video annotation is the labeling of first-person footage — recorded from head-mounted cameras, smart glasses or VR headsets — to mark what the wearer sees, which objects their hands interact with and how each activity unfolds over time.
Because the camera sits close to the wearer’s eyes, this footage shows hands, objects and actions from the same point of view a robot would have. That makes it one of the most useful sources of human demonstration data for embodied AI and robot learning.
Types of egocentric video annotation we deliver
Active-object labels
Which object the person interacts with, per frame.
Hand-object contact events
When contact is made, the type, and when released.
Real2Sim alignment
Metadata to convert real footage to simulation.
IMU & multi-sensor events
Events aligned across sensors to one timestamp.
How Shaip delivers egocentric 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 egocentric 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 is egocentric video annotation used for?
It turns first-person footage into training data for embodied AI and robot learning — labeling active objects, hand-object contact and activity steps so a model can learn tasks from human demonstration.
2. Can Shaip collect egocentric video as well as annotate it?
Yes. We run managed, consent-secured first-person capture with VR headsets and head-mounted cameras across 60+ countries, then annotate the footage in the same engagement — or we annotate footage you already have.
3. How do you handle faces and personal information in first-person footage?
Participants sign consent before capture, de-identification is available, and every dataset ships with auditable provenance under GDPR, SOC 2 Type II and ISO 27001 controls.
4. Can you align video with IMU and other sensor streams?
Yes. We label IMU and multi-sensor events aligned to a single timestamp, so video, motion and other signals line up frame by frame for training.
5. Do you support Real2Sim workflows?
Yes. We add Real2Sim alignment metadata that helps convert real footage into simulation, alongside real2sim pipelines and paired benchmarks to close the sim-to-real gap.
6. How do we start a project with Shaip?
Share your data type, volume and target labels. We scope the egocentric 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.
