Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning

2025-09-19

AI Summary

Defining and monitoring complex robotic activities—comprising flexible, non-predefined sequences of atomic tasks—in dynamic, unstructured environments (e.g., precision agriculture) remains challenging. Method: This paper proposes an end-to-end framework integrating large language models (LLMs), automated planning, and symbolic reasoning. It enables humans to declaratively specify high-level procedures in natural language and supports explainable, real-time tracking and querying of task execution states across past, present, and future temporal scopes. Contribution/Results: To the best of our knowledge, this is the first approach realizing a closed-loop, natural-language-driven pipeline for high-level task planning and semantic-level monitoring—balancing adaptability with safety guarantees. Evaluated in real-world agricultural settings, the system accurately parses natural-language instructions, provides timely execution feedback, and significantly improves human-robot collaboration efficiency and process controllability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Defining complex robot activities in dynamic environments
• Monitoring high-level activities via natural language queries
• Integrating LLMs with automated planning for robotics

Innovation

• LLMs integrated with automated planning
• Natural language specification of activities
• Real-time robot execution monitoring system