Give the Policy Its View Back: Recovering Frozen Visuomotor Policies from Camera Drift

Visuomotor policies can fail under small camera drift even when the task and action space remain unchanged. We study how to recover such policies without updating their weights. We introduce **ReView**, a test-time input calibration method that fits a six-parameter image warp from stored demonstration images and only four unlabeled reset images from the displaced camera. ReView matches early spatial features across the two unpaired image sets, requiring no camera poses, depth, target actions, or interaction rollouts. The fitted warp corrects incoming images while the policy remains frozen. Across six tasks in robomimic and ManiSkill, ReView improves mean success over no adaptation by 28, 22, and 24 percentage points for Diffusion Policy, ACT, and SmolVLA, respectively, and by 27 and 30 percentage points on two real-robot tasks. Ablations show that spatial correspondence is critical for recovery. Our analysis further shows that geometric alignment and policy recovery need not coincide: under parallax, an imperfect scene alignment can better preserve the visual relationships that matter for control. Recovering a policy therefore requires a view it can act on, not necessarily an exact reconstruction of its original view.