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StellaVLA achieves a remarkable 98.8% success rate in adapting VLA models to out-of-distribution tasks using structured demonstrations without human annotation.
Injecting semantic knowledge from vision transformer embeddings into guided depth super-resolution models dramatically reduces artifacts and sharpens depth boundaries.
