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Transforming a single image into a robust simulation environment could revolutionize how robots learn and evaluate policies in real-world scenarios.
By preserving the semantics of pretrained models while achieving superior compositional generalization, InternVLA-A1.5 redefines how robots can learn and execute complex tasks.
