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Experiments show that WholeBodyWAM consistently benefits from increased motion-pretraining scale, improves future-motion prediction and downstream task performance, and transfers effectively to real-world humanoid manipulation.
DIRECT achieves remarkable efficiency gains in sequence labeling by leveraging a template-filling mechanism that minimizes redundant computations while enhancing task alignment with human preferences.