Physical AI Annotation
Human Activity Recognition Annotation for Robots and Physical AI
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 activity recognition annotation?
Activity recognition annotation is the labeling of video with what a person is doing and exactly when — action classes with start and end times — and, for anticipation, the action that comes next.
Annotators mark when each action starts and ends and describe it, usually as a verb and an object — for example, “pour” + “water”. For anticipation, models learn to predict an action shortly before it begins, which helps robots react to people in time.
Types of activity recognition annotation we deliver
Action-class labels
Hierarchical taxonomy — "manipulation" → "pouring liquid".
Anticipation labels
Early motion cues that predict the next action.
Temporal segmentation
Frame-level start/end, including nested actions.
Social-context labels
Conversation, handover, collision avoidance.
How Shaip delivers activity recognition 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 activity recognition 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 activity recognition annotation used for?
It trains models to recognise what a person is doing and predict what they will do next, so robots and Physical AI systems can work safely and helpfully alongside people.
2. Can you use our own action taxonomy?
Yes. We build or adopt a hierarchical taxonomy — for example “manipulation” → “pouring liquid” — during scoping, and calibrate it in the pilot before scaling.
3. How precise are the action boundaries?
Actions are segmented at frame level with start and end times, including nested actions, and every batch goes through multi-layer expert review before delivery.
4. Which environments can you cover?
We have built activity datasets in household, office, factory and cafeteria settings, and can capture new footage across 60+ countries and 10 environment types.
5. Do you label anticipation and human-robot interaction cues?
Yes. Anticipation labels mark the early motion cues that predict the next action, and social-context labels capture conversation, handovers and collision avoidance.
6. How do we start a project with Shaip?
Share your data type, volume and target labels. We scope the activity-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.
