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    "headline": "The Docking Model Found Anomalies on the Way In",
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    "dek": "A GPU-parallel ISS environment trained a camera-and-state world model that doubled held-out-port success over a reinforcement-learning baseline.",
    "summary": "A GPU-parallel ISS environment trained a camera-and-state world model that doubled held-out-port success over a reinforcement-learning baseline.",
    "body_text": "Out-of-this-World-Model learns relative motion and body-fixed camera observations, then predicts future states under thrust and torque commands with per-step uncertainty. In the authors' simulated capsule docking task, it reached 53% success across ports versus 29% for the reinforcement-learning baseline; on held-out ports the comparison was 40% versus 17%. It also classified anomalous objects during approach with 98% accuracy. The environment and model are open source, but these are simulation results rather than flight validation.",
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    "tags": [
      "space robotics",
      "world models",
      "docking"
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      "title": "arXiv preprint 2609.03067",
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      "published_at": "2026-09-02T14:39:40.000-04:00",
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    "title": "The Docking Model Found Anomalies on the Way In",
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