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Learning to predict the visual consequences of actions significantly boosts policy performance and generalization, while simultaneously learning to generate actions sharpens world models.
Action-aligned 3D tracker features can boost VLA policy performance by over 25 points, revolutionizing how robots learn from demonstrations.