Towards a Hybrid LLM/Model-Based Architecture for Robot Coaching: An Instance of Human-Machine Collaboration

Human-Robot Collaboration (HRC) presents significant challenges in assessing situations correctly, adapting robotic behavior to human intentions, ensuring explainability, pertinence, and acceptability, and managing uncertainty. Traditional model-based approaches offer reliability but struggle with human unpredictability and approximate humans with specific models that do not consider all the possible situations. At the same time, probabilistic methods like Large Language Models (LLMs) provide adaptability but lack deterministic guarantees. This paper proposes a hybrid architecture that integrates structured techniques with the flexibility of LLMs to enhance robot coaching in dynamic environments. By bridging deterministic and probabilistic techniques, our architecture aims to advance HRC towards safer, more transparent, flexible, and adaptive interactions. The paper provides a detailed description of the framework’s specifications; however, it should be noted that it has not yet been fully implemented.