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    "headline": "Fifteen Corrections Adapted a Frozen Robot Model",
    "slug": "fifteen-corrections-adapted-a-frozen-robot-model",
    "dek": "Rho kept its flow-matching action expert fixed while a lightweight latent policy learned from feedback.",
    "summary": "Rho kept its flow-matching action expert fixed while a lightweight latent policy learned from feedback.",
    "body_text": "Rho is an open-weights family of vision-language-action models for bimanual manipulation across three dual-arm embodiments. Controlled simulation and physical tests in the report favor embodiment midtraining for downstream adaptation. For online adaptation, a small latent policy learns from corrective feedback to choose observation-conditioned noise for the frozen action expert; the authors say as few as 15 corrected episodes handled situations at the fringe of the offline distribution. Those results belong to the reported tasks and hardware, and the released checkpoints still need independent evaluation.",
    "why_it_matters": "Rho kept its flow-matching action expert fixed while a lightweight latent policy learned from feedback.",
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    "tags": [
      "vision-language-action",
      "bimanual robots",
      "online adaptation"
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    {
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      "title": "arXiv preprint 2609.38164",
      "publisher": "arXiv",
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      "published_at": "2026-09-29T13:59:28.000-04:00",
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    "title": "Fifteen Corrections Adapted a Frozen Robot Model",
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