Physical AI Annotation
Object Detection & Recognition Annotation for Robotics Perception
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 object detection annotation for robotics?
Object detection annotation is the labeling of every object instance in an image or video frame with its class and location — a bounding box or a pixel-level segmentation mask — so a model learns to find and identify objects.
For robotics, two more layers are often added: affordance labels, which show how each part of an object can be used (for example, where to grasp it), and object-state labels, such as open or closed, which change as the robot acts.
Types of object detection annotation we deliver
2D & 3D bounding boxes
Class, confidence and occlusion flag, at frame level.
Instance & semantic segmentation
Pixel masks per instance and per category.
Occlusion labels
Partial/full hidden flags with occlusion %.
Affordance labels
Where/how an object can be interacted with.
Object-state labels
Open/closed, full/empty, on/off, clean/dirty.
How Shaip delivers object detection 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 object detection 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. How is robotics object detection different from standard image annotation?
It is done frame by frame on video of cluttered, changing scenes, and adds robotics-specific labels — affordances and object states — that standard image detection doesn’t capture.
2. Do you provide 2D and 3D annotation?
Yes. We deliver 2D and 3D bounding boxes plus instance and semantic segmentation, and can scope multi-modal projects that combine vision with LiDAR and other sensors.
3. How do you handle occlusion and clutter?
Each object gets a partial or full occlusion flag with an occlusion percentage, and multi-pass expert review checks labels in dense scenes before delivery.
4. Can you add affordance and object-state labels to our existing taxonomy?
Yes. We extend your class list with affordances (where and how an object can be handled) and states such as open/closed, full/empty or on/off, agreed during scoping.
5. How is our data kept secure?
Every project runs under SOC 2 Type II, ISO 27001, ISO 9001:2015, HIPAA and GDPR controls, with an NDA signed on day one, de-identification available and auditable data provenance.
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.
