  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.
