Large Language Models (LLMs) have strong capabilities in natural dialogue, but their inherent indeterminacy presents challenges in robotic environments where safety and reliability are critical. In this study, we propose a dialogue agent that has been developed to guide and support human operators during robot demonstrations, following the Learning from Demonstration (LfD) paradigm, where the robot learns tasks from the operator’s actions. The agent presented in this work extends the standard prompt-based LLM setup by integrating state graphs that explicitly encode dialogue states and transitions. This structure ensures that user interactions follow the intended path, while still allowing users to communicate in a flexible and natural manner. The state graph agent is benchmarked against a monolithic prompt baseline in challenging dialogue scenarios involving ambiguity, incomplete actions, or operator errors. Despite the LLM prompt achieving good standalone performance, the state-controlled agent shows greater contextual understanding, reasoning capability, and advisory performance, leading to more intelligent and reliable interactions.

Human-Machine Interaction (HMI) plays an important role in Industry 5.0, improving worker well-being by automating repetitive tasks and enhancing seamless collaboration between humans and intelligent systems. In this context, Task-Oriented Dialogue (TOD) systems are a commonly used approach to enable natural communication in these settings, traditionally developed using rule-based approaches. However, the revolution of Large Language Models (LLMs) is changing how dialogue systems are being developed without the necessity of relying on tedious and rigid handcrafted rules. Despite their popularity, their application in industrial contexts remains underexplored, necessitating a solution to challenges such as hallucinations, lack of domain-specific data, high training costs, and limited adaptability. In order to explore the contribution of LLMs in the industry field, this work presents LAMIA, a task-oriented dialogue system for industrial scenarios that leverages LLMs through prompt tuning. This system has been adapted and evaluated for a bin-picking use case, using GPT-3.5 Turbo, showing to be an intuitive method for new use cases in Industry 5.0.

In today’s industrial landscape, seamless collaboration between humans and machines is essential and requires a shared knowledge of the operational domain. In this framework, the technical knowledge for operator assistance has traditionally been derived from static sources such as technical documents. However, experienced operators hold invaluable know-how that can significantly contribute to support other operators. This work focuses on enhancing the operator assistance tasks in the manufacturing industry by leveraging spoken natural language interaction. More specifically, a Human-in-the-Loop (HIL) incremental learning approach is proposed to integrate this expertise into a domain knowledge graph (KG) dynamically, along with the use of in-context learning for Large Language Models (LLMs) to benefit other capabilities of the system. Preliminary results of the experimentation carried out in an industrial scenario, where the graph size was increased in a 25%, demonstrate that the incremental enhancing of the KG benefits the dialogue system’s performance.

This work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multilingual resources. This semi-automatic approach allows ontology engineers to exploit available resources so as to associate the potential situations in the use case to FrameNet frames and obtain the relevant lexical units associated to them in the target language, following lexical and semantic criteria, without linguistic expert knowledge. This strategy has been validated and evaluated in two use cases, from industrial scenarios, for interaction in Spanish with a guide robot and with a Computerized Maintenance Management System (CMMS). In both cases, this method has allowed the ontology engineer to instantiate the domain ontology with the intent-relevant information with quality data in a simple and low-resource-consuming manner.