Robotics data for physical AI is the main event in 2026. Along with fierce debates about which foundational model architectures will come on top, @emily_yu , @aurorafeng_01 , and I see an increasing divergence to how people think about the quantity, quality, modality, and diversity of robotic data.
We are currently balancing the sim-to-real gap of synthetic data against the high-friction cost of physical data collection. Our deep take is that the most valuable data infra platforms will match the right data modalities to the right model architectures and evolve alongside the models themselves.
Our market map and open vendor/collector list captures this emerging stack: synthetic data engines, real-world fleet teleoperation, annotation tooling purpose-built for embodied AI, and more.
Comment or DM us if you're building in data infra, if you think we are missing some companies, or have insights on what data inputs you think matter most. Excited to hear from you!
emily yu@emily_yu
robotics data for physical AI is front and center this year. from gtc's heavy focus on data infrastructure to human data ecosystems like egoverse, the field is waking up to the bottleneck of scaling robotics data. and there's real divergence in how people think about quantity, quality, modality, and diversity. @aurorafeng_01, @deeptt and i have been looking at the data infra landscape and put together a market map + open data vendor/collector list 👇
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