video-pre-training

Scaling video pre-training to 120K hours boosts zero-shot success in World Action Models from 36.1% to 77.8% on real robots, enabling faster action prediction.

ZimaBlue introduces a scalable framework for learning generalizable world action models from large-scale egocentric video, substantially improving zero-shot robotic manipulation through a three-stage curriculum and slow-fast architecture.