Shaip has scaled a single ongoing Physical AI data collection program to 5,000 valid hours of egocentric VR motion capture per month. The program supports a humanoid robotics developer and runs on a recurring monthly cycle, covering 300 to 400 tasks across more than 50
-- New York, NY — Aug 13, 2026 — Shaip, a provider of AI training data collection and annotation services, has scaled a single ongoing Physical AI data collection program to 5,000 valid hours of egocentric virtual reality (VR) motion capture per month. The program supports a humanoid robotics developer building sim-to-real systems, in which models trained in simulation transfer to physical robots, and runs on a recurring monthly cycle rather than a one-time collection.
Physical AI refers to artificial intelligence that acts in the physical world through robots and other embodied hardware rather than in software alone. Training it requires recorded human demonstrations of physical tasks, captured with consistent calibration and quality control so developers can segment and evaluate each episode.
Shaip Physical AI collection
Each cycle draws on 1,500 to 2,500 participants and covers 300 to 400 customer-defined tasks spanning locomotion, object manipulation, household and office interaction, and multi-step physical workflows. Capture uses a VR headset paired with five body-mounted motion trackers, producing full-body motion data rather than video alone.
Recording takes place across more than 50 distinct capture settings — individual locations such as a factory assembly bench, an office conference room, a pharmaceutical packaging line, a café espresso station, a warehouse picking aisle or a home kitchen — grouped under six environment types. Setting-level variation matters because a policy trained on one representative room degrades when object placement, lighting and layout differ.
Teams developing vision-language-action (VLA) models need continuous data supply across successive training runs, and pipelines that perform at pilot scale often fail to hold accuracy at recurring high volume. The monthly figure is output on one program, not the limit of available capacity.
"Physical AI programs fail on operational consistency more often than on volume," said Utsav Shah, SVP and Business Head at Shaip. "The task library and the calibration discipline are what let a 5,000-hour monthly cycle stay comparable across thousands of participants and more than 50 capture settings."
Consistency is maintained through a standardised pipeline. Each location is documented with wide-angle photography, configured in an administrative system, submitted for customer review and exported as a scene reference for object placement. QR-linked mapping ties each setting to its recording context. Before every session, all five trackers are paired and validated, and calibration is mandatory for each participant, covering avatar alignment, floor adjustment and boundary setup.
Participants rehearse tasks before recording. Moderators observe through browser-based screencasting and advance to live capture only once sensor behaviour and participant movement meet defined thresholds. Sessions are reviewed for motion clarity, task correctness, scene alignment and sensor accuracy, with unusable recordings retaken before counting toward valid hours. Delivery details are documented in the Shaip case study.
Task selection draws on a standing library of more than 1,400 documented scenarios across industrial, hospitality, retail, office, residential, agriculture, automotive and construction environments. Each specifies task goal, guidelines, objects, initial setup, expected outcome, duration, difficulty tier, tool use and safety notes, with episodes running 0.5 to 10 minutes. Standardised scenarios give consistent initial states and comparable success criteria across participants and cycles — properties VLA and imitation-learning pipelines need for episode segmentation. Clients select from the library or commission new scenarios, as described on the Physical AI data catalog page.
About Shaip
Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. Shaip supports Physical AI, Healthcare AI, Computer Vision, Generative AI, and Conversational AI use cases for leading AI labs and enterprise customers worldwide. Learn more at shaip.com
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