Advances in large language models (LLMs) are profoundly reshaping the field of human-robot interaction (HRI). While prior work has highlighted the technical potential of LLMs, few studies have systematically examined their human-centered impact (e.g., human-oriented understanding, user modeling, and levels of autonomy), making it difficult to consolidate emerging challenges in LLM-driven HRI systems. Therefore, we conducted a systematic literature search following the PRISMA guideline, identifying 86 articles that met our inclusion criteria. Our findings reveal that: (1) LLMs are transforming the fundamentals of HRI by reshaping how robots sense context, generate socially grounded interactions, and maintain continuous alignment with human needs in embodied settings; and (2) current research is largely exploratory, with different studies focusing on different facets of LLM-driven HRI, resulting in wide-ranging choices of experimental setups, study methods, and evaluation metrics. Finally, we identify key design considerations and challenges, offering a coherent overview and guidelines for future research at the intersection of LLMs and HRI.

翻译：大型语言模型（LLMs）的进展正在深刻重塑人机交互（HRI）领域。尽管已有研究强调了LLMs的技术潜力，但少有工作系统性地考察其以人为中心的影响（例如，面向人的理解、用户建模及自主性水平），这使得整合LLM驱动的HRI系统中新出现的挑战变得困难。因此，我们遵循PRISMA指南进行了系统性文献检索，筛选出86篇符合纳入标准的文章。我们的研究发现：（1）LLMs通过重塑机器人在具身环境中感知情境、生成基于社会背景的交互以及持续保持与人类需求对齐的方式，正在改变HRI的基础；（2）当前研究在很大程度上是探索性的，不同研究聚焦于LLM驱动HRI的不同方面，导致实验设置、研究方法和评估指标的选择范围广泛。最后，我们指出了关键的设计考量与挑战，为LLMs与HRI交叉领域的未来研究提供了连贯的概览与指导原则。