This new notion for how to train humanoids arguably began with the launch of ChatGPT in 2022. Large language models were able to generate text through exposure to massive amounts of training data—every word ever written that AI companies could find (or, some argue, steal). Roboticists wanted to apply these scaling laws to robotics but lacked an internet-size collection of data describing how we move.
Put off by how difficult this would be to amass, companies used workarounds, like teaching robots to move in virtual simulations. However, simulations never perfectly model how things like friction or elasticity work in the real world, so the robots trained in them tended to (literally) stumble.
Now companies building humanoid robots have decided that collecting real-world data, as cumbersome as it is, could yield a massive payoff. That’s where things got weird.
Early efforts were quaint and academic. Labs collected hours and hours of data from people doing household tasks, like flipping waffles or cleaning their desks, while wearing cameras or handheld grippers. The data was shared openly. But as venture capital money poured into robotics—$6.1 billion in 2025 for humanoids alone—the race to create this training data has gotten more competitive, and more elaborate.
