It's amazing how a few JEPA studies can point the way toward answering a question: What does a machine need to predict well enough to plan what to do next?

JEPA's general point is to predict what matters without recreating every pixel. I explored 3 variants - H-JEPA, DeepJEPA and RoboJEPA, and each approaches the question from a different angle:

• Planning at distinct levels, from fine local details to big picture
• Learning when a prediction needs a second look (more computation)
• and Scaling up JEPA’s predictor to make better predictions, and so better actions

I’d like to see a robot that brings these ideas together and see how that would change its actions.

There’s a lot more to explain about these three JEPAs, so I go through each one in this video: https://www.youtube.com/watch?v=YPP_qApESg0