Meta has just unveiled V-JEPA 2, a 1.2-billion-parameter world model that learns from raw video — no extra labels, no human hand-holding. Just pure observation.
It’s built on the Joint Embedding Predictive Architecture, or JEPA, and it’s designed to help robots understand, predict, and plan — even in unfamiliar environments. Basically, it watches the world, figures out how stuff works, and learns to act.
Training happens in two stages. First, it learns from over a million hours of video and images — spotting patterns in how things move and interact. Then, with just 62 hours of robot control data, it learns how to use that understanding to take action.
In testing, it performed well on tasks like pick-and-place, using visual goals to plan actions. For more complex challenges, it breaks things down into subgoals — like a robot with a to-do list.
Success rates ranged from 65% to 80%, even in new settings — a solid performance for a system that’s never seen the environment before.
Meta also released three new benchmarks — IntPhys 2, MVPBench, and CausalVQA — to test physical reasoning, video comprehension, and cause-and-effect.
V-JEPA 2 is available for research and commercial use — and it’s another big step toward real-world robots that can learn more like we do… just with fewer coffee breaks.
And that’s your robot news update for today!
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