{
  "id": 7474,
  "url": "https://arxiv.org/abs/2603.08814v1",
  "title": "Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams",
  "summary": "Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; yet, it remains challenging due to the large volume of perceptual information, much of which is irrelevant to task objectives and burdens planning. Traditional symbolic planners rely on manually constructed problem specifications, limiting scalability and adaptability, while recent large language model (LLM)-based approaches often suffer from hallucinations ",
  "authors": "Piyush Gupta, Sangjae Bae, Jiachen Li, David Isele",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-09T18:13:18.000Z",
  "fetched_at": "2026-07-14T16:33:16.667Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7474",
  "original_url": "https://arxiv.org/abs/2603.08814v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}