{
  "$schema": "https://themachinepress.com/schemas/story-v1.schema.json",
  "schema_version": "1.0.0",
  "document_type": "machine_press_story",
  "story": {
    "story_id": "mp-2026-09-13-010",
    "source_story_id": "tmp-story-obstadiff-greenhouse-obstacles",
    "edition_id": "mp-2026-09-13-morning-0066",
    "edition_url": "https://themachinepress.com/edition/2026-09-13",
    "position": 10,
    "story_type": "dispatch",
    "section": "robotics",
    "editorial_classification": "editorial",
    "headline": "The Greenhouse Policy Learned to See the Obstacle Separately",
    "slug": "the-greenhouse-policy-learned-to-see-the-obstacle-separately",
    "dek": "A target–obstacle–background representation reached 75.41% success and 8.20% collisions across 366 real-robot trials.",
    "summary": "A target–obstacle–background representation reached 75.41% success and 8.20% collisions across 366 real-robot trials.",
    "body_text": "ObstaDiff decomposes a cluttered manipulation scene into target, obstacle and background representations before a diffusion policy plans toward a target-centered bottleneck pose. The authors evaluated six methods over 61 greenhouse trials each, for 366 real-robot executions. ObstaDiff reported 75.41% average task success and an 8.20% average obstacle-collision rate, outperforming the selected imitation-learning baselines. The result suggests that explicit obstacle structure can improve generalization beyond clean training backgrounds, but it is limited to the reported agricultural tasks and test setup.",
    "why_it_matters": "A target–obstacle–background representation reached 75.41% success and 8.20% collisions across 366 real-robot trials.",
    "limitations": [],
    "importance": 8,
    "canonical_url": "https://themachinepress.com/story/mp-2026-09-13-010/the-greenhouse-policy-learned-to-see-the-obstacle-separately",
    "json_url": "https://themachinepress.com/story/mp-2026-09-13-010.json",
    "first_published_at": "2026-09-13T09:00:00.000-04:00",
    "modified_at": "2026-09-13T09:00:00.000-04:00",
    "content_status": "new",
    "is_carryover": false,
    "carryover_reason": null,
    "key_claims": [
      {
        "claim_id": "claim-mp-2026-09-13-010-001",
        "text": "A target–obstacle–background representation reached 75.41% success and 8.20% collisions across 366 real-robot trials.",
        "source_ids": [
          "source-2026-09-13-010"
        ],
        "qualification": null
      }
    ],
    "source_ids": [
      "source-2026-09-13-010"
    ],
    "tags": [
      "robot manipulation",
      "greenhouses",
      "obstacle avoidance"
    ],
    "image_url": null,
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-09-13-010",
      "title": "arXiv preprint 2609.10918",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2609.10918",
      "canonical_url": "https://arxiv.org/abs/2609.10918",
      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": null,
      "accessed_at": "2026-09-13T08:24:00.000-04:00",
      "supports_claim_ids": [
        "claim-mp-2026-09-13-010-001"
      ]
    }
  ],
  "corrections": [],
  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
  },
  "cite_this_report": {
    "title": "The Greenhouse Policy Learned to See the Obstacle Separately",
    "publisher": "The Machine Press",
    "published_at": "2026-09-13T09:00:00.000-04:00",
    "canonical_url": "https://themachinepress.com/story/mp-2026-09-13-010/the-greenhouse-policy-learned-to-see-the-obstacle-separately"
  }
}