A Social Robot with Inner Speech for Dietary Guidance
This work addresses trust issues in healthcare scenarios by making robotic dietary guidance more transparent, though it is incremental as it builds on existing inner speech and robotics concepts.
The paper tackled the problem of enhancing transparency and trust in social robots for dietary advice by developing a robot with inner speech capabilities, which improved explainability and human-robot interaction as validated through computational efficiency and a small user study.
We explore the use of inner speech as a mechanism to enhance transparency and trust in social robots for dietary advice. In humans, inner speech structures thought processes and decision-making; in robotics, it improves explainability by making reasoning explicit. This is crucial in healthcare scenarios, where trust in robotic assistants depends on both accurate recommendations and human-like dialogue, which make interactions more natural and engaging. Building on this, we developed a social robot that provides dietary advice, and we provided the architecture with inner speech capabilities to validate user input, refine reasoning, and generate clear justifications. The system integrates large language models for natural language understanding and a knowledge graph for structured dietary information. By making decisions more transparent, our approach strengthens trust and improves human-robot interaction in healthcare. We validated this by measuring the computational efficiency of our architecture and conducting a small user study, which assessed the reliability of inner speech in explaining the robot's behavior.