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Uncertainty-Guided Refinement (UGR), a sparse refinement framework for chunk-based visuomotor policies, which first predicts a full action chunk, estimates per-step temporal uncertainty from the coarse hidden states, and applies residual correction only to the most uncertain timesteps selected by a binary mask.
ARP not only aligns visual observations with action representations but also refines execution precision, leading to unprecedented performance in robotic manipulation tasks.
UMI-Bench 1.0 reveals that standardized real-world evaluations can dramatically improve the reliability of UMI-style robotic manipulation policies.
