Human models play a crucial role in human-robot interaction (HRI), enabling robots to consider the impact of their actions on people and plan their behavior accordingly. However, crafting good human models is challenging; capturing context-dependent human behavior requires significant prior knowledge and/or large amounts of interaction data, both of which are difficult to obtain. In this work, we explore the potential of large-language models (LLMs) -- which have consumed vast amounts of human-generated text data -- to act as zero-shot human models for HRI. Our experiments on three social datasets yield promising results; the LLMs are able to achieve performance comparable to purpose-built models. That said, we also discuss current limitations, such as sensitivity to prompts and spatial/numerical reasoning mishaps. Based on our findings, we demonstrate how LLM-based human models can be integrated into a social robot's planning process and applied in HRI scenarios. Specifically, we present one case study on a simulated trust-based table-clearing task and replicate past results that relied on custom models. Next, we conduct a new robot utensil-passing experiment (n = 65) where preliminary results show that planning with a LLM-based human model can achieve gains over a basic myopic plan. In summary, our results show that LLMs offer a promising (but incomplete) approach to human modeling for HRI.

翻译：人类模型在人机交互（HRI）中发挥着关键作用，使机器人能够考虑其行为对人的影响，并据此规划自身行为。然而，构建良好的类人模型颇具挑战性；捕捉依赖上下文的人类行为需要大量先验知识和/或海量交互数据，而这两者都难以获取。在本研究中，我们探索了大型语言模型（LLM）——这类模型已消耗了大量人类生成的文本数据——作为HRI中零样本人类模型的潜力。我们在三个社会数据集上的实验取得了令人鼓舞的结果：LLM能够达到与专门构建的模型相当的性能。尽管如此，我们也讨论了当前的局限性，例如对提示词的敏感性以及空间/数值推理中的失误。基于我们的发现，我们展示了如何将基于LLM的人类模型集成到社交机器人的规划过程中，并应用于HRI场景。具体而言，我们呈现了一个基于模拟信任的桌面清理任务的案例研究，复现了以往依赖自定义模型的结果。随后，我们进行了一项新的机器人递餐具实验（n=65），初步结果表明，基于LLM人类模型进行规划相较于基本短视规划能够取得性能提升。总之，我们的结果证明，LLM为HRI中的人类建模提供了一种有前景（但尚不完善）的途径。