Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning

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. I. INTRODUCTION In recent years, there has been a significant increase in the interest and demand for automating complex and labor-intensive activities through the deployment of robotic systems. These activities, often encountered in industrial and agricultural domains, are typically composed of multiple, smaller atomic subtasksthat must be coordinated to achieve a larger goal. What makes these environments particularly challenging is their dynamic and unpredictable nature: the specific sequence and combination of tasks required can change based on real-time conditions, external events, or evolving objectives. Importantly, while the range of possible jobs is usually limited and known in advance, the structure and flow of the overall activity are not fixed and therefore must be adapted on the fly. In such contexts, human operators must maintain a clear understanding of ongoing high-level activities, both to ensure correctness and to make timely decisions. This includes being aware of what the system has done (past), what it is currently doing (present), and what it intends to do next (future). However, this kind of situational awareness is difficult to maintain in the absence of mechanisms for querying the system in a human-friendly way.