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OpTTA: Test-time adaptation of world models for planning, not just prediction

The paper proposes OpTTA, which adds the planner's goal-reaching cost term to the test-time adaptation objective for world models, keeping the model optimistic about reaching the goal. Under visual and environmental shifts in manipulation, reaching, and navigation tasks, when only the predictor is adapted, OpTTA raises the final planning success rate by an average of 13.5 percentage points, compared with 6.9 percentage points for AdaJEPA, which uses only the prediction objective; when the encoder is also adapted, the two achieve 13.5 and 7.5 percentage points, respectively.

Part of the event: OpTTA: Incorporating Planning Objectives into World Model Adaptation

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