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Can a realistic generated robot video still be bad training data? This episode explains how RoboCurate turns predicted robot actions back into visible motion, then uses that comparison to curate synthetic demonstrations. We follow the path from video generation and inverse dynamics to a learned consistency judge, focusing on downstream policy performance as the meaningful test of a data curator. Three findings anchor the discussion: the gain from adding action verification after video screening; transfer across simulation benchmarks; and synthetic-only novel-task examples in physical-robot training. The experiments span GR-1 Tabletop and DexMimicGen in simulation, plus dexterous manipulation with ALLEX and OpenArm. The central limitation is that replay excludes manipulated objects: motion agreement does not itself validate contact or grasp stability. For practitioners building synthetic-data pipelines, the takeaway is to evaluate the pairing of video and action, not visual plausibility alone. Where should your next compute budget go — another generated dataset, or a better test of its labels?
