Physical AI Solutions
Physical AI Data Ops for Robotics and Embodied AI Teams
Collect, annotate, validate, and deliver training-ready multimodal datasets for robotics, autonomy, and vision-language-action models — with enterprise-grade quality, human-in-the-loop review, and flexible output formats aligned to your training pipeline.
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The Physical AI Data Bottleneck
Robots aren’t failing because of hardware. Actuators, sensors, and compute have outpaced the data needed to use them. There is no internet-scale corpus for physical intelligence — no equivalent of the web text that trained LLMs. Every grasp, step, and task correction has to be captured, structured, and validated from the real world.
That’s the gap Shaip closes: real-world human demonstration and multi-sensor data, collected and annotated at the scale embodied AI training actually demands.
Full-Stack Physical AI Training Data
From raw data collection through RLHF and evaluation — one partner across every layer your team needs.
Egocentric & Teleoperation Data Collection
Global-scale multimodal data capture—including images, video, audio, sensor data, telematics, robot demonstrations, human-guided trajectories, & task recordings.
Read moreMulti-sensor VLA/action Annotation
Objects, actions, tracking, segmentation, intent, spatial context, motion, and human-machine interactions — structured ground truth at every layer.
Read moreSynthetic Data Generation & Support
Synthetic dataset generation, QA, enrichment, validation, taxonomy alignment, and sim-to-real readiness workflows — originating quality data at scale, not just checking it.
RLHF & Preference Learning
Human preference collection, comparison ranking, reward model training data, and behavior alignment workflows — structured to move physical AI from functional to trustworthy.
Evaluation & Benchmarks
Regression sets, edge-case libraries, safety-scenario coverage, and release-readiness benchmarks purpose-built for physical AI systems.
Human-in-the-loop Review
Expert validation, exception handling, QA, and continuous feedback loops that improve reliability and close the gap between model outputs and retraining.
Physical AI Annotation, Done by Domain Experts
Beyond collection, our specialist teams label robotics and embodied-AI data across six annotation domains — kept frame-accurate and synchronized to a single timestamp.
Vision-Language-Action (VLA)
Instruction-action pairs, task decomposition into timestamped sub-steps, and grounding labels.
Explore VLA annotationObject Detection & Segmentation
Instance & semantic masks, occlusion %, affordance and object-state labels.
Explore object detection annotationActivity
Recognition
Temporal segmentation, nested actions, and social-context labels like handovers.
Explore activity recognition annotationEgocentric Annotation
Hand-object contact events, IMU/multi-sensor alignment, and Real2Sim metadata.
Explore egocentric annotationManipulation & Grasping
Force & contact points, pre-grasp approach parameters, and failure/recovery labels.
Explore grasping annotationSafety & Anomaly
Collision-risk severity, human-proximity zones (ISO 10218 / ISO/TS 15066), recovery behaviors.
Explore safety annotationData collection & annotation for every Physical AI use case
From first-person behavior capture to multi-sensor simulation pipelines — Shaip collects and annotates the data your specific system needs, at the scale and quality deployment demands.
Humanoid Robot Demonstration Learning
Capture step-by-step human task demonstrations using head-mounted cameras and hand tracking to build ground truth for imitation learning across warehouse picking, assembly, and kitchen workflows.
Egocentric Activity Capture & Real2Sim Pipelines
Build first-person datasets through VR headsets, head-mounted cameras, and wearables for walking, picking, cooking, and assembly tasks, structured for direct training or simulation conversion.
Multi-Sensor Fusion Data Collection
Manage synchronized Vision, IMU, LiDAR, and Audio collection pipelines with setup, timing alignment, QA, and annotation workflows for autonomous robotics and spatial AI systems.
Autonomous Systems Edge Case Collection
Capture rare and high-risk operational scenarios such as occlusions, low-light conditions, and crowded environments to improve model performance where generic datasets fall short.
Smart Glasses & Wearable AI Training
Collect real-world POV datasets from smart glasses and mixed reality devices for object recognition, context understanding, gaze mapping, and spatial UI interaction labeling.
Industrial Safety & Compliance Monitoring
Capture worker behavior across factories, oil and gas, and construction sites for PPE detection, unsafe action identification, ergonomics review, and event-level annotation.
Healthcare & Rehabilitation Motion Data
Support gait analysis, therapy movement tracking, and elderly monitoring with 42-keypoint skeleton annotation, joint angle analysis, movement phase tagging, and fall-risk labeling.
AR/VR Interaction & Gesture Training
Create gesture-rich datasets for pointing, grabbing, and scrolling interactions using VR headsets with hand and eye tracking across mixed reality ecosystems.
Other Supported Physical AI Use Cases
- Robotic manipulation and pick-place tasks
- Navigation and mobility systems
- Warehouse, logistics, and industrial robotics
- Embodied assistants and service robots
- Human-robot interaction datasets
- Action-conditioned vision-language models
- Multi-step task execution and behavioral cloning workflows
- Teleoperation and human-guided demonstration capture
Global collection footprint - Real environments. Not lab data.
Physical AI models fail in the real world when they’re trained only on clean, curated lab footage. Shaip’s collector network captures data from the actual surfaces where your model will operate — across consumer, retail, industrial, and mobility environments.
What Separates Shaip from Every Other AI Data Provider
Not a point annotator. Not a crowdsourcing platform. The integrated data infrastructure layer your physical AI team has been missing.
End-to-end infrastructure: from point annotation to real-world collection, synthetic data generation, RLHF-grade validation, and safety-scenario benchmarks — all under one engagement.
Global collection at scale: demonstrations, human activity, and real-world scenario capture across geographies, environments, and task types — managed, not crowdsourced.
Multi-modal annotation depth: vision, LiDAR, language, action, and workflow context — structured for how physical AI actually trains, evaluates, and gets to deployment.
Managed workforce and quality infrastructure: credentialed domain experts, structured QA workflows, ISO, SOC 2, and HIPAA-ready certifications — built for deployment-grade accuracy.
In-person + real-world environments: Controlled studio capture and live real-world environments — both available, both managed. Custom scenarios and edge case generation included.
Successful Stories
The Data Ops Backbone Behind a 10,000-Hour Humanoid Robotics Motion Data
Sim-to-real learning needs more than volume — it needs grounded, calibrated, task-validated motion data at scale. For one humanoid robotics customer, Shaip built the end-to-end data ops backbone: QR-mapped scene setup, five-sensor tracking, moderated rehearsal, and model-ready QA — generating 10,000 hours of egocentric VR motion data across ~4,000 participants and 100 tasks in just 30 days.
Security & Compliance
Ready to build physical AI that actually deploys?
Talk to Shaip about multimodal data infrastructure, synthetic data generation, RLHF, evaluation workflows, and human-in-the-loop validation for robotics, autonomy, and embodied AI.
Contact UsFrequently Asked Questions (FAQ)
How does Shaip source physical AI training data legally and ethically?
All Shaip data is collected under signed participant consent with documented data-rights and usage terms. We operate controlled in-studio capture, real-world field collection, and in-home programs — each with its own consent framework aligned to GDPR, CCPA, HIPAA, and regional privacy standards. We don’t scrape, we don’t repurpose public video, and every dataset ships with an auditable provenance record for enterprise legal review.
How fast can Shaip deliver a first dataset for a physical AI pilot?
Typical pilot timelines run in weeks from signed brief to first-batch delivery, depending on collection environment, sensor stack, and participant requirements. Studio-based demonstrations and egocentric captures are generally faster; multi-sensor fusion programs with LiDAR and calibrated rigs take longer.
Can Shaip support sim-to-real workflows?
Shaip delivers real-world capture, synthetic-data generation, and real2sim pipelines — with structured validation loops to close the sim-to-real gap. This includes domain-randomized synthetic augmentation, edge-case injection, and paired real + synthetic benchmarks.
What sensor modalities does Shaip capture and annotate?
Camera (RGB, monochrome, event), depth (stereo, structured light, ToF), LiDAR, IMU, radar, audio, force/torque, hand tracking, eye tracking, GPS, and telematics. All channels delivered time-synchronized with calibration metadata.
Does Shaip support teleoperation-based data collection?
Yes. Shaip designs and manages teleoperation capture programs — operator recruitment and training, rig setup, task scripting, episode-level QA, and trajectory annotation. Teleoperated demonstrations are delivered time-synchronized with camera, joint-state, and force/torque channels, formatted for imitation learning and VLA fine-tuning pipelines.
How does Shaip handle edge cases and safety-critical scenarios for physical AI?
Shaip maintains structured taxonomies for edge-case collection — occlusion, low-light, adverse weather, high-density environments, atypical actor behavior, and rare-event scripting. Deliverables include regression test sets, release-readiness benchmarks, and safety-scenario coverage mapped to deployment risk tiers.
What compliance certifications does Shaip hold?
ISO 27001, SOC 2 Type II, HIPAA-ready controls, GDPR. Additional compliance frameworks are implemented per-program where required.
How does Shaip manage quality for physical AI annotations?
Shaip operates a tiered QA pipeline: Ubiquity QA for first-pass validation, CPA (Shaip Review) for gold-set calibration, and Shaip Validation for final release review. Inter-annotator agreement, consensus review, and task-specific acceptance thresholds are configured per project.
Does Shaip deliver RLHF and preference data for physical AI?
Yes. Human preference collection, comparison ranking, reward model training data, and behavior-alignment workflows — scoped for robotics policies, VLA alignment, and video-generation reward models.
