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    "story_id": "mp-2026-09-07-008",
    "source_story_id": "tmp-story-sociogesture-robot-social-cues",
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    "position": 8,
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    "section": "robotics",
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    "headline": "The Robot Learned the Difference Between a Command and a Refusal",
    "slug": "the-robot-learned-the-difference-between-a-command-and-a-refusal",
    "dek": "A compact body-and-hand model recognizes invitations, refusals and unavailability in real time, then saves uncertain encounters for later adaptation.",
    "summary": "A compact body-and-hand model recognizes invitations, refusals and unavailability in real time, then saves uncertain encounters for later adaptation.",
    "body_text": "SocioGesture combines confidence-aware body and hand skeleton streams and trains with simulated occlusion so missing hands or unstable keypoints do not add inference cost. On a mixed indoor-outdoor dataset, the authors report strong held-out-subject recognition and improved robustness under structured joint occlusion while running on a robot-mounted edge device. Uncertain segments are retained for offline labeling and vocabulary expansion. The results cover the collected gesture set, not unrestricted social understanding.",
    "why_it_matters": "A compact body-and-hand model recognizes invitations, refusals and unavailability in real time, then saves uncertain encounters for later adaptation.",
    "limitations": [
      "SocioGesture combines confidence-aware body and hand skeleton streams and trains with simulated occlusion so missing hands or unstable keypoints do not add inference cost.",
      "The results cover the collected gesture set, not unrestricted social understanding."
    ],
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    "first_published_at": "2026-09-07T09:00:00.000-04:00",
    "modified_at": "2026-09-07T09:00:00.000-04:00",
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        "text": "A compact body-and-hand model recognizes invitations, refusals and unavailability in real time, then saves uncertain encounters for later adaptation.",
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        "qualification": "SocioGesture combines confidence-aware body and hand skeleton streams and trains with simulated occlusion so missing hands or unstable keypoints do not add inference cost."
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    "source_ids": [
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    "tags": [
      "human-robot interaction",
      "gesture recognition",
      "edge AI"
    ],
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    {
      "source_id": "source-2026-09-07-008",
      "title": "arXiv preprint 2609.04545",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.04545",
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      "source_type": "primary_research",
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      "published_at": "2026-09-03T19:03:43.000-04:00",
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    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Robot Learned the Difference Between a Command and a Refusal",
    "publisher": "The Machine Press",
    "published_at": "2026-09-07T09:00:00.000-04:00",
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