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    "story_id": "mp-2026-10-06-004",
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    "section": "robotics",
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    "headline": "The Warehouse Map Learned When the Same Box Was Different",
    "slug": "the-warehouse-map-learned-when-the-same-box-was-different",
    "dek": "FOCUS combined geometry and rendered appearance to distinguish persisted, replaced and removed objects across robot visits.",
    "summary": "FOCUS combined geometry and rendered appearance to distinguish persisted, replaced and removed objects across robot visits.",
    "body_text": "A long-running robot can return to a shelf whose geometry looks unchanged even though one object has been replaced by a similar instance. FOCUS treats that ambiguity as probabilistic inference, combining geometric evidence with appearance rendered from a 3D Gaussian map and maintaining three states for each object: persisted, replaced or removed. On an Isaac Sim warehouse benchmark built around ambiguous substitutions, the paper reports replacement F1 rising from 0.20 to 0.84 over a probabilistic baseline. It also reports transfer to the real-world TorWIC data without manual parameter retuning. The work is less about seeing change once than maintaining a memory that knows what kind of change it saw.",
    "why_it_matters": "FOCUS combined geometry and rendered appearance to distinguish persisted, replaced and removed objects across robot visits.",
    "limitations": [],
    "importance": 7,
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    "tags": [
      "robot memory",
      "change detection",
      "3D Gaussian splatting"
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  "sources": [
    {
      "source_id": "source-2026-10-06-004",
      "title": "arXiv preprint 2610.05639",
      "publisher": "arXiv",
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      "published_at": "2026-10-04T20:30:00.000-04:00",
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    "name": "The Machine Press",
    "url": "https://themachinepress.com",
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  "cite_this_report": {
    "title": "The Warehouse Map Learned When the Same Box Was Different",
    "publisher": "The Machine Press",
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