How Do We Research Human-Robot Interaction in the Age of Large Language Models? A Systematic Review

• URL: http://arxiv.org/abs/2602.15063v1
• Date: Fri, 13 Feb 2026 13:55:43 GMT
• Title: How Do We Research Human-Robot Interaction in the Age of Large Language Models? A Systematic Review
• Abstract summary: Large language models (LLMs) are profoundly reshaping the field of human-robot interaction (HRI)<n>We conducted a systematic literature search following the PRISMA guideline, identifying 86 articles that met our inclusion criteria.<n>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.
• Score: 25.744333061791284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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