In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating Large Language Models (LLMs) and skilfully leveraging the powerful reasoning and instruction-following capabilities of these types of models to achieve advanced human-like affective and cognitive capabilities. This approach is novel compared to the current state-of-the-art LLM-based agents which do not implement human-like long-term memory or sophisticated emotional appraisal. The naturalness of social robots, consisting of multiple modules, highly depends on the performance and capabilities of each component of the system and the seamless integration of the components. We built a social robot system that enables generating appropriate behaviours through multimodal input processing, bringing episodic memories accordingly to the recognised user, and simulating the emotional states of the robot induced by the interaction with the human partner. In particular, we introduce an LLM-agent frame for social robots, SoR-ReAct, serving as a core component for the interaction module in our system. This design has brought forth the advancement of social robots and aims to increase the quality of human-robot interaction.

翻译：本文阐述了为娜丁社交机器人平台开发智能鲁棒社交机器人系统的方法。我们通过集成大语言模型（LLMs），并巧妙利用此类模型强大的推理与指令跟随能力，实现了先进的类人情感与认知功能。相较于当前未实现类人长期记忆或精细情感评估的先进LLM智能体，本方法具有创新性。由多模块构成的社交机器人，其自然度高度依赖于系统各组成部分的性能与能力以及模块间的无缝集成。我们构建的社交机器人系统能够通过多模态输入处理生成适宜行为，根据识别用户调用情景记忆，并模拟人机交互引发的机器人情感状态。特别地，我们提出了面向社交机器人的LLM智能体框架SoR-ReAct，作为系统中交互模块的核心组件。该设计推动了社交机器人技术的进步，旨在提升人机交互质量。