Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916957958176768 |
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| author | Argenziano, Francesco Umili, Elena Leotta, Francesco Nardi, Daniele |
| author_facet | Argenziano, Francesco Umili, Elena Leotta, Francesco Nardi, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning Argenziano, Francesco Umili, Elena Leotta, Francesco Nardi, Daniele Robotics Human-Computer Interaction 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. |
| title | Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.16006 |