Large Language Models as Zero-Shot Human Models for Human-Robot Interaction

• URL: http://arxiv.org/abs/2303.03548v2
• Date: Wed, 02 Oct 2024 00:57:37 GMT
• Title: Large Language Models as Zero-Shot Human Models for Human-Robot Interaction
• Authors: Bowen Zhang, Harold Soh,
• Abstract summary: Large-language models (LLMs) can act as zero-shot human models for human-robot interaction. LLMs achieve performance comparable to purpose-built models. We present one case study on a simulated trust-based table-clearing task.
• Score: 12.455647753787442
• License:
• Abstract: 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.

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