{
  "id": 18285,
  "url": "https://arxiv.org/abs/2608.08884v1",
  "title": "SHRIMP: Iterative Refinement of Robot Task Plans",
  "summary": "As collaborative robots have entered domains such as manufacturing, agriculture, and healthcare, programming or adapting robot behavior typically requires robotic expertise that most end users lack. Natural language lowers this barrier. Recent advancements in large language models (LLMs) have made it feasible to translate natural language into robot task plans. However, language-based task specification suffers from semantic ambiguity, and generative models lack transparency for how language ins",
  "authors": "Mya Schroder, Yuna Hwang, Callie Y. Kim, Leqian Cheng, Jeffrey Li-cheng Liu, Chenchen Zheng, Xinning He, Bilge Mutlu",
  "category": "research",
  "topics": "healthcare,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-09T19:47:16.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18285",
  "original_url": "https://arxiv.org/abs/2608.08884v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}