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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.
HAF outperforms conventional VLA models in humanoid loco-manipulation by effectively managing complex motion coordination without the need for extensive computational resources.
Forget sharing user data – FedCRF uses federated learning and item semantics to achieve cross-domain recommendations even when user bases don't overlap.
