Latent world models follow reliable scaling laws: imagination error (how well the model predicts future states) improves predictably with compute, and this directly correlates with real robot task performance, making it possible to forecast model quality without expensive robot experiments.
RoboJEPA is an 8-billion-parameter world model trained on real robot data from 12 different embodiments. It predicts future states in a compressed latent space and follows predictable scaling laws—meaning you can estimate how much better it gets with more compute before actually training it. The model works as a zero-shot robot controller, planning by imagining future states toward a goal image.