Removing unfamiliar visual distractors from the input observation before world-model rollout, then compositing them back into predicted frames, makes action verification robust and raises real-robot pick-and-place success from 20% to 70%.
Flip: Flow-centric generative planning as general-purpose manipulation world model
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Reimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control
Removing unfamiliar visual distractors from the input observation before world-model rollout, then compositing them back into predicted frames, makes action verification robust and raises real-robot pick-and-place success from 20% to 70%.