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JAMB, a diffusion policy that jointly denoises bimanual actions and future 3D point tracks within a shared Transformer, and shows stronger generalization to cluttered scenes and out-of-distribution backgrounds than the evaluated baselines.
Today's robot policies and VLAs fall apart when faced with unexpected challenges requiring reasoning, strategy adaptation, and robustness, even after fine-tuning on similar tasks.
