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Beckmann World Models: Direct Terminal-Map Learning for One-Step Action-Conditioned Prediction
Not provided in the abstract
video predictionaction conditioningmachine learningworld models
2610.26903
Builder Relevance
2h ago80%
Abstract
The paper introduces Beckmann World Models (BWM), which learn a direct map from noise, visual history, and actions to future frames, achieving significant improvements in one-step action-conditioned video prediction.
Reality Card
Core Claim
BWM reduces 64-frame prediction MSE by 68% on PushT and 57% on Robomimic Can compared to a regression baseline, while also achieving the lowest mean IoU error among evaluated models.
Method / Result
Achieves 3.3 times higher throughput and 78% lower peak memory than local one-step DriftWorld.
Limitations
Transport consistency on interpolated inputs does not guarantee accurate predictions from pure noise at inference.
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