How Do We Research Human-Robot Interaction in the Age of Large Language Models? A Systematic Review
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915801095733248 |
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| author | Wang, Yufeng Xu, Yuan Nikolova, Anastasia Wang, Yuxuan Wang, Jianyu Wang, Chongyang Tong, Xin |
| author_facet | Wang, Yufeng Xu, Yuan Nikolova, Anastasia Wang, Yuxuan Wang, Jianyu Wang, Chongyang Tong, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15063 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Do We Research Human-Robot Interaction in the Age of Large Language Models? A Systematic Review Wang, Yufeng Xu, Yuan Nikolova, Anastasia Wang, Yuxuan Wang, Jianyu Wang, Chongyang Tong, Xin Robotics Human-Computer Interaction 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. |
| title | How Do We Research Human-Robot Interaction in the Age of Large Language Models? A Systematic Review |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.15063 |