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    "story_id": "mp-2026-08-19-002",
    "source_story_id": "tmp-feature-lambda-hold-musculoskeletal-sprint",
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    "headline": "The Muscles Learned to Sprint by Holding Still",
    "slug": "the-muscles-learned-to-sprint-by-holding-still",
    "dek": "A physiology-inspired controller held per-muscle threshold lengths across gait phases and trained a simulated skeleton to sprint with a minimal reward.",
    "summary": "A physiology-inspired controller held per-muscle threshold lengths across gait phases and trained a simulated skeleton to sprint with a minimal reward.",
    "body_text": "A muscle-actuated skeleton has far more control variables than a conventional rigid-body character, making reinforcement-learning exploration slow and unstable. The proposed lambda-hold controller reduces that burden by setting an equilibrium-point threshold length for each muscle and holding it over part of the gait cycle; a stretch-reflex rule then turns those thresholds into muscle excitation without requiring the policy to issue every low-level command.\n\nThe authors report that this controller learned human-like sprinting within an hour of training using only a minimal task reward. The work connects an engineering control scheme to the equilibrium-point hypothesis, intermittent control and optimal feedback control, but it remains predictive musculoskeletal simulation. It does not establish a clinical model of human movement or prove that the learned controller matches biological motor control in people.",
    "why_it_matters": "A physiology-inspired controller held per-muscle threshold lengths across gait phases and trained a simulated skeleton to sprint with a minimal reward.",
    "limitations": [
      "The authors report that this controller learned human-like sprinting within an hour of training using only a minimal task reward.",
      "It does not establish a clinical model of human movement or prove that the learned controller matches biological motor control in people."
    ],
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    "first_published_at": "2026-08-19T09:00:00.000-04:00",
    "modified_at": "2026-08-19T09:00:00.000-04:00",
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        "text": "A physiology-inspired controller held per-muscle threshold lengths across gait phases and trained a simulated skeleton to sprint with a minimal reward.",
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        "qualification": "The authors report that this controller learned human-like sprinting within an hour of training using only a minimal task reward."
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    "source_ids": [
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    "tags": [
      "musculoskeletal simulation",
      "reinforcement learning",
      "motor control",
      "sprinting"
    ],
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  "sources": [
    {
      "source_id": "source-2026-08-19-002",
      "title": "arXiv preprint 2608.17030",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.17030",
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      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-08-16T20:00:00.000-04:00",
      "accessed_at": "2026-08-19T08:22:00.000-04:00",
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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 Muscles Learned to Sprint by Holding Still",
    "publisher": "The Machine Press",
    "published_at": "2026-08-19T09:00:00.000-04:00",
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