Action-Space Velocity Correction for Visual Consistency in Robotic Flow Policies

Visuomotor policies trained under one visual appearance degrade in performance when lighting, color, or background change during inference. Retraining or augmenting the policy for every new appearance is costly. In this paper, we introduce Action-Space Velocity Correction (ASVC), an inference-time correction to the velocity of a flow-matching policy that corrects based on a reference view of the observation. Transport conditioned on reference views provides consistency of visual variation in action space. ASVC modifies only the integration of a frozen policy, requiring no retraining and applying as an add-on to any flow-matching action policy. Our method is validated on a toy reaching task and on the DMC-GB2 benchmark across three flow-matching base policies: ReinFlow, GFP, and Diffusion Policy. We further deploy ASVC on a Trossen WidowX AI robot for a cube-stacking task with variation in nearby distractor objects and lighting. Across these settings, ASVC recovers much of the return lost under color and video shifts while performance on the training appearance is preserved.