系统梳理大模型如何重塑人机交互，揭示其核心影响与研究挑战
How Do We Research Human-Robot Interaction in the Age of Large Language Models? A Systematic Review
- 基于PRISMA指南检索86篇论文，系统分析大模型在人机交互中的应用
- 大模型推动机器人感知上下文、生成社交互动、持续对齐人类需求
- 当前研究分散，缺乏统一实验设计与评估标准，亟需整合框架
大语言模型（LLMs）的进展正在深刻改变人机交互（HRI）领域。尽管已有研究关注其技术潜力，但很少有系统性工作探讨其以用户为中心的影响（如人类理解、用户建模和自主性水平），导致难以整合大模型驱动的HRI系统中涌现的挑战。为此，我们遵循PRISMA指南进行了系统文献检索，共识别出86篇符合纳入标准的文章。研究发现：（1）大模型正在重塑HRI的基本范式，通过改变机器人在具身环境中感知上下文、生成社会适切互动以及持续对齐人类需求的方式；（2）当前研究仍以探索性为主，各研究聚焦于大模型驱动HRI的不同方面，导致实验设置、研究方法和评估指标差异显著。最后，我们提炼出关键设计考量与挑战，为大模型与人机交互交叉领域的未来研究提供连贯性综述与指导。
原文摘要 · Abstract (English)
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.
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