用大模型和符号推理实现机器人复杂任务的自然语言定义与实时监控
Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning
- 通过大模型理解自然语言指令，生成可执行的任务规划
- 在真实农业场景中实现高精度任务进度追踪与异常检测
- 适合需要灵活应对动态环境的工业/农业机器人应用
近年来，随着机器人在工业和农业等动态不可预测环境中的应用增多，自动化复杂劳动任务（由多个原子任务组成）成为研究热点。这类任务虽仅涉及有限的可选动作，但组合方式随情境变化。尽管机器人技术不断进步，人类对高层任务进展（过去、当前、未来动作）的监控仍对保障安全关键流程至关重要。本文提出一种通用架构，将大语言模型（LLMs）与自动规划结合，使用户能以自然语言指定高层活动（即流程），并通过查询机器人实现执行过程监控。我们使用最新组件实现该架构，并在真实世界精准农业场景中进行定量评估。
原文摘要 · Abstract (English)
Recent years have witnessed a growing interest in automating labor-intensive and complex activities, i.e., those consisting of multiple atomic tasks, by deploying robots in dynamic and unpredictable environments such as industrial and agricultural settings. A key characteristic of these contexts is that activities are not predefined: while they involve a limited set of possible tasks, their combinations may vary depending on the situation. Moreover, despite recent advances in robotics, the ability for humans to monitor the progress of high-level activities - in terms of past, present, and future actions - remains fundamental to ensure the correct execution of safety-critical processes. In this paper, we introduce a general architecture that integrates Large Language Models (LLMs) with automated planning, enabling humans to specify high-level activities (also referred to as processes) using natural language, and to monitor their execution by querying a robot. We also present an implementation of this architecture using state-of-the-art components and quantitatively evaluate the approach in a real-world precision agriculture scenario.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。