Juno: Do Predictive Latents Make VLA Manipulation More Robust?
Can predictive world models improve VLA manipulation without interfering with action learning? This episode examines how Juno connects control-aligned JEPA representations, future-latent supervision, and deployment adaptation—and which conclusions its experiments actually support. We cover three questions: why motion-sensitive global representations need spatial features alongside them; what the ablations reveal about the interaction between feature fusion and predictive reasoning; and why learning dynamics from failed executions is not the same as learning successful behavior from failures…
The skinny
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