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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.
Automated bias labeling in forestry can be achieved with 50% less expert input while outperforming conventional ML methods.
