Physical AI Annotation
VLA Annotation Services for Vision-Language-Action Robot Models
Robots must connect words to what they see and do. Shaip’s experts link every instruction to the right object and action for accurate training data.
What is VLA (vision-language-action) annotation?
VLA annotation is the labeling of robot demonstration data so that each natural-language instruction is linked to the camera observations and robot actions that carry it out — the training format used by vision-language-action (VLA) models.
Vision-language-action models take camera images and a written instruction as input and produce robot actions as output. To learn this, they need large sets of robot demonstrations in which every instruction is matched to what the camera saw and what the robot did — so the accuracy of each link shapes what the robot learns.
Types of VLA annotation we deliver
Instruction-action pairs
A command paired with the exact video segment showing the action.
Task decomposition
"Make coffee" → ordered, timestamped sub-steps.
Grounding labels
"Red cup" → bounding box; "left" → spatial position.
Negative examples
Wrong object, direction or grip — what not to do.
How Shaip delivers VLA 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 VLA 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 VLA annotation used for?
It produces the training data for vision-language-action models: each natural-language instruction is linked to the video segment, objects and robot actions it describes, so the model learns to turn a command into the correct motion.
2. Can Shaip annotate our existing robot demonstrations, or do you also collect them?
3. How do you make sure each instruction matches the right object and action?
Bilingual annotators with language and visual-reasoning skills label every pair, then a three-stage review checks instruction alignment, grounding (the right object and position) and action correspondence before delivery.
4. Can you follow our instruction style, task taxonomy and label schema?
Yes. Scoping maps your taxonomy, schema and edge cases, and the pilot calibrates guidelines against your expectations before we scale. Data is delivered in the format your training pipeline needs.
5. Do you label negative and failure examples?
Yes. We add negative examples — wrong object, wrong direction, wrong grip — so the model learns what not to do, not only what a correct execution looks like.
6. How do we start a project with Shaip?
Share your data type, volume and target labels. We scope the instruction-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.
