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    "story_id": "mp-2026-08-21-002",
    "source_story_id": "tmp-feature-hybrid-feedback-sampling-mpc",
    "edition_id": "mp-2026-08-21-morning-0043",
    "edition_url": "https://themachinepress.com/edition/2026-08-21",
    "position": 2,
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    "section": "robotics",
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    "headline": "The Robot Sampled Widely, Then Let Feedback Choose",
    "slug": "the-robot-sampled-widely-then-let-feedback-choose",
    "dek": "A hybrid model-predictive controller combined global motion search with an optimized feedback policy and handled unstable humanoid tasks in real-world tests.",
    "summary": "A hybrid model-predictive controller combined global motion search with an optimized feedback policy and handled unstable humanoid tasks in real-world tests.",
    "body_text": "Sampling-based model-predictive control can search many possible action sequences in parallel, but its sample needs grow rapidly with planning horizon on high-dimensional, open-loop unstable systems. Feedback Sampling MPC starts from a different proposal distribution: actions are sampled through an optimized feedback policy, then a hybrid design balances local stabilization against global exploration according to the system and compute budget.\n\nThe authors prove faster convergence than standard MPPI and better optimality than feedback sampling alone under their assumptions. In contact-rich humanoid locomotion, loco-manipulation and dexterous-manipulation tasks, the method handled unstable cases where conventional sampling struggled and outperformed feedback-only policies. They also report real-world humanoid locomotion and manipulation demonstrations. Those experiments show a practical control strategy on tested platforms; they do not establish general-purpose autonomy or safety outside the reported tasks.",
    "why_it_matters": "A hybrid model-predictive controller combined global motion search with an optimized feedback policy and handled unstable humanoid tasks in real-world tests.",
    "limitations": [
      "Feedback Sampling MPC starts from a different proposal distribution: actions are sampled through an optimized feedback policy, then a hybrid design balances local stabilization against global exploration according to the system and compute budget.",
      "In contact-rich humanoid locomotion, loco-manipulation and dexterous-manipulation tasks, the method handled unstable cases where conventional sampling struggled and outperformed feedback-only policies.",
      "Those experiments show a practical control strategy on tested platforms; they do not establish general-purpose autonomy or safety outside the reported tasks."
    ],
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    "first_published_at": "2026-08-21T09:00:00.000-04:00",
    "modified_at": "2026-08-21T09:00:00.000-04:00",
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        "qualification": "Feedback Sampling MPC starts from a different proposal distribution: actions are sampled through an optimized feedback policy, then a hybrid design balances local stabilization against global exploration according to the system and compute budget."
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    "source_ids": [
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    "tags": [
      "humanoid robots",
      "model predictive control",
      "feedback sampling",
      "manipulation"
    ],
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  "sources": [
    {
      "source_id": "source-2026-08-21-002",
      "title": "arXiv preprint 2608.19443",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.19443",
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      "source_type": "primary_research",
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      "published_at": "2026-08-18T20:00:00.000-04:00",
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    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Robot Sampled Widely, Then Let Feedback Choose",
    "publisher": "The Machine Press",
    "published_at": "2026-08-21T09:00:00.000-04:00",
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