用大模型把模糊指令转为机器人协作计划，还能根据反馈动态调整。
From Vague Instructions to Task Plans: A Feedback-Driven HRC Task Planning Framework based on LLMs
- 用大模型从自然语言指令中生成任务计划，支持多场景通用。
- 通过实时反馈迭代优化计划，确保符合人类意图。
- 仅需简洁提示词即可适配多种任务，降低使用门槛。
大语言模型在人机协作（HRC）中的规划潜力已得到验证，能通过自然语言输入推理生成结构化计划，具备跨任务泛化能力并可适应人类指令。本文研究了大模型在人机协作任务规划中的应用，重点关注其从高层、模糊的人类输入中进行推理，并基于实时反馈进行计划微调的能力。提出一种新型混合框架，结合大模型与人类反馈，生成动态、上下文感知的任务计划。研究表明，仅用一个简洁的提示词即可适用于多种任务和环境，克服了以往研究中依赖冗长详细结构化提示的局限。通过将用户偏好融入规划循环，确保生成计划不仅有效，且与人类意图对齐。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have demonstrated their potential as planners in human-robot collaboration (HRC) scenarios, offering a promising alternative to traditional planning methods. LLMs, which can generate structured plans by reasoning over natural language inputs, have the ability to generalize across diverse tasks and adapt to human instructions. This paper investigates the potential of LLMs to facilitate planning in the context of human-robot collaborative tasks, with a focus on their ability to reason from high-level, vague human inputs, and fine-tune plans based on real-time feedback. We propose a novel hybrid framework that combines LLMs with human feedback to create dynamic, context-aware task plans. Our work also highlights how a single, concise prompt can be used for a wide range of tasks and environments, overcoming the limitations of long, detailed structured prompts typically used in prior studies. By integrating user preferences into the planning loop, we ensure that the generated plans are not only effective but aligned with human intentions.
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