Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental health challenges. The large body of work on SAR demonstrates its potential to provide at-home support that complements clinic-based interventions delivered by mental health professionals, making these interventions more effective and accessible. However, there are still several major technical challenges that hinder SAR-mediated interactions and interventions from reaching human-level social intelligence and efficacy. With the recent advances in large language models (LLMs), there is an increased potential for novel applications within the field of SAR that can significantly expand the current capabilities of SARs. However, incorporating LLMs introduces new risks and ethical concerns that have not yet been encountered, and must be carefully be addressed to safely deploy these more advanced systems. In this work, we aim to conduct a brief survey on the use of LLMs in SAR technologies, and discuss the potentials and risks of applying LLMs to the following three major technical challenges of SAR: 1) natural language dialog; 2) multimodal understanding; 3) LLMs as robot policies.

翻译：社会辅助型机器人（SAR）在为特殊需求群体（如老年人、自闭症谱系障碍儿童及心理健康挑战者）提供个性化认知情感支持方面已取得显著成效。大量研究工作表明，SAR具有提供居家支持的潜力，可补充心理健康专业人员实施的临床干预，使这些干预措施更有效且更易获取。然而，当前仍存在若干重大技术挑战，制约着SAR介导的交互与干预达到人类水平的社会智能与效能。随着大语言模型（LLM）的最新进展，其在SAR领域的创新应用潜力日益增强，有望显著扩展SAR的现有能力。但引入LLM也带来了前所未见的新型风险与伦理问题，必须审慎应对以安全部署这些更先进的系统。本研究旨在对LLM在SAR技术中的应用进行简要综述，探讨将LLM应用于SAR三大主要技术挑战（1）自然语言对话；（2）多模态理解；（3）将LLM作为机器人策略的潜力与风险。