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Robust out-of-distribution transfer is achieved by decoupling demonstration matching from policy learning, allowing robots to generalize effectively to unseen objects.
GeniWorld achieves robust zero-shot generalization in robotic manipulation, outperforming traditional models even with minimal training data.
Object-centric mask conditioning in MaskWAM dramatically improves policy performance, outperforming traditional WAMs by effectively reducing language ambiguity in complex environments.
