让机器人像人一样‘自言自语’，提升饮食指导的可信度。
A Social Robot with Inner Speech for Dietary Guidance
- 用内部语言模拟人类思维过程，让机器人推理可见
- 用户研究显示内省式对话显著提升信任感与可靠性
- 适合医疗辅助、智能健康助手场景使用
我们探索将内省言语作为增强社交机器人在饮食建议中透明度与信任感的机制。在人类中，内省言语有助于组织思维和决策；在机器人中，它通过使推理过程显式化来提升可解释性。这在医疗场景中尤为重要，因为用户对机器人助手的信任不仅依赖于建议准确性，还取决于类人对话带来的自然感与参与感。为此，我们开发了一款提供饮食建议的社交机器人，并引入内省言语能力，用于验证用户输入、优化推理并生成清晰理由。系统整合大语言模型实现自然语言理解，以及知识图谱支持结构化饮食信息。通过使决策过程更透明，该方法增强了用户信任，改善了人机交互体验。我们通过评估架构计算效率并开展小规模用户研究，验证了内省言语在解释机器人行为方面的有效性。
原文摘要 · Abstract (English)
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.
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