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  "story": {
    "story_id": "mp-2026-10-16-004",
    "source_story_id": "tmp-story-spark0-tactile-pretraining",
    "edition_id": "mp-2026-10-16-morning-0098",
    "edition_url": "https://themachinepress.com/edition/2026-10-16",
    "position": 4,
    "story_type": "dispatch",
    "section": "robotics",
    "editorial_classification": "editorial",
    "headline": "A Thousand Hours of Touch Improves What the Robot Sees",
    "slug": "a-thousand-hours-of-touch-improves-what-the-robot-sees",
    "dek": "Spark-0 uses glove-derived contact maps and tactile histories during training, then supports both vision-only and touch-conditioned deployment.",
    "summary": "Spark-0 uses glove-derived contact maps and tactile histories during training, then supports both vision-only and touch-conditioned deployment.",
    "body_text": "Contact-rich manipulation hides force, compliance and incipient slip from ordinary cameras. Spark-0 pretrains visual tokens with contact maps from tactile gloves and a frozen 3D geometry teacher, while a separate path encodes force histories and taxel states. The authors built a 1,000-hour HumanTouch dataset and evaluated the model in simulation and on a real dexterous hand. Against vision-only pretraining on the same data and compute, keeping tactile supervision raised reported Ego Humanoid success from 52.5 to 71.7 percent; using touch at deployment raised SparkArena-Real success from 44 to 52 percent. These are benchmark results on the stated embodiments, not proof of general dexterity.",
    "why_it_matters": "Spark-0 uses glove-derived contact maps and tactile histories during training, then supports both vision-only and touch-conditioned deployment.",
    "limitations": [
      "Against vision-only pretraining on the same data and compute, keeping tactile supervision raised reported Ego Humanoid success from 52.5 to 71.7 percent; using touch at deployment raised SparkArena-Real success from 44 to 52 percent.",
      "These are benchmark results on the stated embodiments, not proof of general dexterity."
    ],
    "importance": 8,
    "canonical_url": "https://themachinepress.com/story/mp-2026-10-16-004/a-thousand-hours-of-touch-improves-what-the-robot-sees",
    "json_url": "https://themachinepress.com/story/mp-2026-10-16-004.json",
    "first_published_at": "2026-10-16T09:00:00.000-04:00",
    "modified_at": "2026-10-16T09:00:00.000-04:00",
    "content_status": "new",
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    "carryover_reason": null,
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        "text": "Spark-0 uses glove-derived contact maps and tactile histories during training, then supports both vision-only and touch-conditioned deployment.",
        "source_ids": [
          "source-2026-10-16-004"
        ],
        "qualification": "Against vision-only pretraining on the same data and compute, keeping tactile supervision raised reported Ego Humanoid success from 52.5 to 71.7 percent; using touch at deployment raised SparkArena-Real success from 44 to 52 percent."
      }
    ],
    "source_ids": [
      "source-2026-10-16-004"
    ],
    "tags": [
      "tactile sensing",
      "dexterous manipulation",
      "pretraining"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-10-16-004",
      "title": "arXiv preprint 2610.22342, original v1",
      "publisher": "arXiv / submitting research authors",
      "url": "https://arxiv.org/abs/2610.22342v1",
      "canonical_url": "https://arxiv.org/abs/2610.22342v1",
      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-10-15T13:56:17.000-04:00",
      "accessed_at": "2026-10-16T08:26:36.400-04:00",
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  "publisher": {
    "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": "A Thousand Hours of Touch Improves What the Robot Sees",
    "publisher": "The Machine Press",
    "published_at": "2026-10-16T09:00:00.000-04:00",
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