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Robotic manipulation and scene prediction are unified in a single 3D-native world action model that directly reasons about metric space, achieving SOTA performance across diverse tasks.
Forget hand-engineered reward shaping: ACDC learns robotic manipulation tasks faster and more reliably by adaptively balancing exploration and exploitation with a dynamic contrastive loss.
