# Designing Social Robots with LLMs for Engaging Human Interaction
## 2 Background  
Human-robot interaction (HRI) research has progressively evolved from task-oriented dialogues toward more socially engaging, emotionally resonant interactions. Early systems, such as ELIZA [15], illustrated the potential of language-based human-machine communication, but relied heavily on predefined scripts [16; 17], offering little conversational flexibility. Later transactional chatbots increased responsiveness [18; 19], but continued to lack the fluidity, nuance, and adaptability that are characteristic of human dialogue.  
In response, the field shifted towards context-aware and multimodal dialogue systems [20; 21; 22; 23]. These systems integrated features such as nonverbal cues, facial expression analysis, and vocal prosody [24; 25], aiming to enrich interaction quality. However, many still struggled with capturing cultural subtleties, humour, or emotional alignment [26; 27]--factors essential to building trust and sustained engagement, especially in socially assistive contexts.  
The emergence of LLMs, including GPT-3 and GPT-4 [28; 29], marks a substantial shift in open-domain dialogue capabilities. LLMs can generate coherent, contextually appropriate responses at scale, opening up new opportunities for personalised and dynamic robot interaction [13]. Consequently, recent studies have begun to explore the integration of LLMs into social robots, often focusing on technical feasibility or short-term interaction scenarios.  
For instance, [PERSON] et al. [30] integrated GPT-3 into a storytelling companion for children, while [PERSON] et al. [31] explored short well-being conversations between older adults and the QT robot. [PERSON] et al. [14] used GPT-3.5 in a Furhat-based setup to co-design social companion behaviours with elderly users. While these studies highlight the potential of LLMs in socially assistive contexts, they were typically constrained to single-session use, lacked persistent memory mechanisms, and did not support real-time speech streaming, limiting their suitability for sustained, naturalistic interaction.  
Other efforts, such as those by [PERSON] et al. [32] and [PERSON] et al. [33], have explored LLM integration into Pepper and Nao robots. However, these systems were either not evaluated with end users or lacked conversational memory and persona continuity. [PERSON] et al. [34] introduced Nadine, a memory-equipped humanoid designed for long-term engagement, though its use remained confined to lab demonstrations.  
Research has also explored affective expression and social presence. [PERSON] et al. [35] linked GPT-3.5 output to real-time emotional expression in a Furhat robot to enhance perceived warmth. Yet, many of these implementations relied on scripted memory, lacked adaptive persona control, or did not support multilingual voice interaction--features that are especially relevant in elderly care scenarios.  
Across this body of work, several technical and design challenges remain unresolved, including high response latency [36; 37], hallucinated content, poor alignment with user expectations [38], and limited support for multilingual and dialectal variation [39]. Furthermore, very few systems have been tested with both younger and older adult users in real-world settings.  
Our work contributes to this growing area by presenting a fully integrated, LLM-powered social robot designed for elderly interaction, featuring the following: (i) real-time audio streaming for responsive dialogue; (ii) layered memory summarisation to support continuity across sessions; (iii) prompt-level persona conditioning for conversational consistency; and (iv) multilingual speech processing tailored to regional accents. Unlike prior studies, we evaluate the system with both young and older adults in realistic interaction settings. While limitations such as hallucinations and latency persist, our approach offers new design strategies to support coherent, memory-aware interaction in socially assistive contexts.