EgoScale trains a 22-DoF humanoid mostly on 20,000 hours of human video

Researchers trained a humanoid with 22-DoF dexterous hands mainly on over 20,000 hours of egocentric human video, with no robot in the loop, to perform tasks such as assembling model cars and folding shirts.

They report a log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and state that this loss predicts real-robot success rate.

The recipe, called EgoScale, pre-trains GR00T N1.5 on the video, adds only 4 hours of robot play data, and reports a 54% gain over training from scratch across five dexterous tasks.