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Set representation models can be made robust to inference-time corruptions like outliers and missing data by training against a learned barycentric adversary.
One model to control them all: Qwen-VLA achieves impressive zero-shot generalization across diverse robotic tasks and embodiments by unifying vision-language-action modeling.
