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UMI-Bridge, which uses UMI as an intermediate domain to align representations according to action equivalence rather than pixel similarity, supports action-anchored latent alignment for data-efficient robot learning and UMI-to-robot task transfer.
Safety compliance can vary by over 28 points among LLMs with similar predictive accuracy, revealing hidden risks in flight predictions.
Achieving 98.6% accuracy in 3D Gaussian world modeling could redefine efficiency benchmarks for robotic manipulation tasks.
