用大模型让多机器人协作找物，懂上下文还能自动分任务。
Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models
- 用大模型理解自然语言指令，结合机器人本地空间知识拆解任务。
- 在实验中成功分配47/50次，远超随机（28/50）和常识方法（26/50）。
- 适合需要多机器人协同、处理模糊或临时任务的智能系统场景。
执行如“找一个苹果和一根香蕉”或“为野外考察做准备”这类指令，需要搜索多个物品或理解上下文依赖的命令，具有挑战性。本研究解决多机器人协作时的任务分配问题：每个机器人拥有不同区域的现场空间知识。我们提出一种基于大语言模型（LLMs）与空间概念的任务规划框架，将自然语言指令分解为子任务并分配给相应机器人。设计了一种新的少样本提示策略，使LLM能从模糊指令中推断所需物品并合理拆解任务。实验中该方法达成47/50次成功分配，优于随机分配（28/50）和基于常识的分配（26/50）。此外，通过两台实际移动机械臂的定性评估，结果表明该框架可有效处理涉及临时类别（如“为野外考察做准备”）的指令，完成任务分解、分配、顺序规划与执行。
原文摘要 · Abstract (English)
It is crucial to efficiently execute instructions such as "Find an apple and a banana" or "Get ready for a field trip," which require searching for multiple objects or understanding context-dependent commands. This study addresses the challenging problem of determining which robot should be assigned to which part of a task when each robot possesses different situational on-site knowledge-specifically, spatial concepts learned from the area designated to it by the user. We propose a task planning framework that leverages large language models (LLMs) and spatial concepts to decompose natural language instructions into subtasks and allocate them to multiple robots. We designed a novel few-shot prompting strategy that enables LLMs to infer required objects from ambiguous commands and decompose them into appropriate subtasks. In our experiments, the proposed method achieved 47/50 successful assignments, outperforming random (28/50) and commonsense-based assignment (26/50). Furthermore, we conducted qualitative evaluations using two actual mobile manipulators. The results demonstrated that our framework could handle instructions, including those involving ad hoc categories such as "Get ready for a field trip," by successfully performing task decomposition, assignment, sequential planning, and execution.
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