Scaling Physical AI and Humanoid Robotics with 10K Hours of Sim-to-Real Motion Data
How Shaip delivered 10,000 hours of egocentric VR motion-capture data across 4,000 participants, 100 tasks, and 5+ real-world environments — built as a production-grade Physical AI training data pipeline for sim-to-real humanoid robotic.
Project Overview
As Physical AI and humanoid robotics move into real-world deployment, the client needed a scalable framework to collect 10,000 hours of task-based VR motion data across diverse environments with consistent calibration, execution, and QA.
Shaip built the end-to-end data operations pipeline covering scene setup, QR mapping, five-sensor tracking, participant rehearsal, moderated capture, and review workflows to support 100 customer-defined tasks and deliver model-ready embodied AI datasets at scale.
Key Stats
Participants
~4,000
Data Volume
10,000 valid hours
Environment Coverage
Office, Home, Factory, Café, Warehouse etc.
Timeline
1 month
Challenges
- Scaling motion data collection from controlled pilot-style workflows into a 10,000-hour, multi-environment program.
- Maintaining consistent tracking accuracy across varied real-world scenes and participant setups.
- Ensuring each session met strict requirements for APK/version control, shared network setup, screencasting, and sensor pairing.
- Managing 100 customer-defined tasks across categories such as locomotion, object manipulation, household interaction, office interaction, and multi-step physical workflows—each requiring correct scene setup, object placement, participant readiness, and moderator-led validation.
- Converting raw sessions into model-ready outputs through repeatable QA, retake handling, and upload review workflows.
Solution
Collection Strategy
Shaip designed a scalable collection framework for 10,000 valid hours of VR motion data, delivered in milestone-based batches. Based on the source planning ratio of 3–5 participants per 10 valid hours, the full program scales to an estimated 3,000–5,000 participants, with ~4,000 participants used as the midpoint planning figure.
Environment & Scene Management
Each capture location was treated as structured scene. Shaip documented the environment using wide-angle room photography, configured scenes in the admin system, coordinated customer review, and exported Scene PDFs for physical placement. QR-linked scene mapping ensured that every real-world environment could be reliably tied to the correct recording context.
Device & Application Readiness
Shaip standardized technical readiness by ensuring the VR headset and monitoring device were connected to the same network, controlling APK installation/update flow, and enabling browser-based screencasting for moderator visibility throughout the session.
Motion Tracking & Calibration
Before each session, all five motion trackers were paired and validated. Calibration was mandatory for every participant, including avatar alignment checks, floor adjustment, and custom boundary setup to ensure accurate full-body motion capture within the recordable activity space.
Task Execution & Moderation
Participants were guided through scene-specific task preparation and rehearsal before the recording. Moderators observed via screencast, verified task accuracy and motion clarity & only advanced to live capture once sensor behavior & participant movement met quality expectations. Recording start/stop was executed through the defined gesture workflow.
Quality Assurance & Model-Ready Outputs
After recording, sessions were uploaded in history for review. Shaip validated motion clarity, task correctness, scene alignment, and sensor accuracy, canceling or retaking unusable recordings when required. This created a more dependable path toward annotation-ready, QA-verified, model-ready datasets for embodied AI & robotics training.
Project Scope
| Dataset Type | Participants | Recording Volume | Environments | Task Volume | Capture Setup | Timeline |
|---|---|---|---|---|---|---|
| Egocentric VR motion capture | ~4,000 | 10,000 valid hours | Office, Home, Café, Factory, Warehouse and additional real-world environments | 100 customer-defined tasks | VR headset + 5 motion trackers | 1 month |
The Outcome
- Established a scalable data operations framework for 10,000 hours of Physical AI training data
- Standardized scene governance, QR-based mapping, and five-sensor calibration across distributed environments
- Improved collection consistency through moderated rehearsal, real-time screencast review, and session-level QA
- Enabled task-validated, annotation-ready outputs for downstream embodied AI, simulation, and robotics model development
- Strengthened the client’s sim-to-real data pipeline with high-quality egocentric motion capture
from diverse real-world environments
Overall, Shaip helped transform a complex VR capture requirement into a structured, production-ready data pipeline — one capable of supporting Physical AI, embodied intelligence, & humanoid robotics initiatives with stronger consistency, traceability, and scale.
Shaip helped us build the data operations backbone for our Physical AI roadmap. Their team brought structure to multi-environment motion capture, participant management, scene setup, calibration, & QA — enabling us to generate model-ready datasets that support sim-to-real learning for embodied AI and humanoid robotics.
– VP, Data & Simulation Infrastructure
