Title: Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning Show Chinese title

Title: 通过大语言模型和符号推理定义和监控复杂的机器人活动

Abstract: 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. Show Chinese abstract

Abstract: 近年来，人们越来越关注通过在动态和不可预测的环境中部署机器人来自动化劳动密集型和复杂的活动，即由多个基本任务组成的活动，例如工业和农业环境。 这些情境的一个关键特征是活动不是预先定义的：虽然它们涉及有限的一组可能的任务，但它们的组合可能会根据情况而变化。 此外，尽管机器人技术最近取得了进展，但人类在过去的、现在的和未来的动作方面监控高层次活动的能力仍然是确保安全关键过程正确执行的基本要素。 在本文中，我们引入了一种通用架构，将大型语言模型（LLMs）与自动规划相结合，使人类能够使用自然语言指定高层次活动（也称为过程），并通过查询机器人来监控其执行。 我们还使用最先进的组件实现了该架构，并在现实世界的精准农业场景中对方法进行了定量评估。