We're building a kinesthetic and kinematic data warehouse for training humanoid robotics systems — using the same operational machinery we've spent years refining for human-subject research.
Large language models learned from a vast, pre-existing body of human text. Robots learning to operate in the physical world have no equivalent. Physical interaction data — the kind that captures how a hand actually grips, hesitates, and corrects — is scarce, hard to collect, and even harder to collect well.
Video footage and casual motion capture exist, but they're low-fidelity and difficult to reconcile with the precision a robotics model actually needs. Generating the right kind of data requires structured, repeatable, high-quality human-subject capture — the same operational discipline that consumer research has always required, pointed at a different output.
Every major AI lab pursuing robotics runs into this same bottleneck: building a reliable human-data collection operation is slow, expensive, and far outside most labs' core expertise. That gap is the opportunity.
Our team has spent years running structured human-subject data collection at a professional standard, for clients including HBO and Meta — recruiting, facility operations, rigorous protocol design, and reporting at scale. That's not a dataset that transfers to robotics directly. It's an operational capability that does: the ability to design, recruit for, and run high-fidelity human data collection reliably, at quality, and on schedule.
K-Data repoints that machinery toward task-based kinesthetic and kinematic capture — structured human movement data designed specifically for training humanoid systems. The resulting warehouse is made accessible to customers through proprietary tools and a usage-based API, so robotics teams can draw on it the way they'd draw on any other infrastructure provider.
Standing up a kinematic data warehouse means treating data collection as a discipline, not a one-time capture exercise.
Working with robotics partners to identify which physical tasks and motions are actually valuable to capture first.
Using our existing recruiting infrastructure to source and train the human subjects who generate the motion data.
Structured, repeatable capture sessions run to the same standard our biometric research practice has always required.
Captured motion is cleaned, annotated, and made available through proprietary tools and a usage-based API.
A structured, high-fidelity alternative to scraped or low-quality motion data — built by a team that already knows how to run this kind of operation at quality.
If you're working on humanoid systems and thinking about where your training data comes from, we'd like to hear what you need before the warehouse is fully built.
Or email us directly at hello@peakk.io