SHRIMP: Iterative Refinement of Robot Task Plans
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
Record details
Published: 9 August 2026
Source: arXiv cs.HC
Category: Research
Topics: Healthcare · Agents & autonomy · Transparency
Retrieved: 11 August 2026
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ethics.ai (9 August 2026), “SHRIMP: Iterative Refinement of Robot Task Plans,” evidence record 18285, https://ethics.ai/record/18285 (originally published by arXiv cs.HC).
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