A Social Robot with Inner Speech for Dietary Guidance

TL;DR

This paper presents a social robot equipped with inner speech capabilities, leveraging large language models and knowledge graphs to enhance transparency, trust, and natural interaction in dietary guidance within healthcare settings.

Contribution

It introduces an architecture that integrates inner speech into social robots for healthcare, improving explainability and user trust in dietary advice.

Findings

Inner speech improves robot transparency and user trust.

The system effectively integrates language models and knowledge graphs.

User study shows positive perception of robot explanations.

Abstract

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.