Frontier AI labs
Force-aware training data for vision-language-action and world models.
Force data for robotic manipulation.
Preload pairs everyday video with synchronized muscle signal, capturing how hard human hands hold, press, and let go.
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The problem
There is no internet of data for manipulation
Robotics teams are collecting egocentric video to learn from human demonstrations. But video alone cannot measure how much force a hand applies. A gentle grip and a crushing squeeze can look almost identical on camera. One million hours is still just 0.1% of a billion-hour benchmark. Closing that gap means collecting more experience and making each recording more useful. Robots need to learn when to grip firmly, ease off, or respond to slipping. Without synchronized force data, they must infer that interaction from images alone.
Sources: Apple, NVIDIA, DYNA. Log scale; filled columns show orders of magnitude.
Our solution
Add the force channel with synchronized EMG
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Collect
Capture everyday video with synchronized EMG.
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Clean
Filter and annotate the data for quality.
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Evaluate
Measure how much the data improves a model.
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Sell
Deliver force-enriched datasets to robotics teams.
Through this process, we create validated datasets of synchronized video and muscle signals for robot learning.
Who we serve
Teams whose models have to touch the world
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Get in touch
Help us reinvent robotics inference.