Large Language Models as Zero-Shot Human Models for Human-Robot Interaction, Bowen Zhang★, Harold Soh★, arXiv preprint, 2023
Links: Paper | Github

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.


You can find our paper here. Check out our repository here on github


Please consider citing our paper if you build upon our results and ideas.

Bowen Zhang★, Harold Soh★, “Large Language Models as Zero-Shot Human Models for Human-Robot Interaction”, arXiv preprint, 2023

url = {},
author={Zhang, Bowen and Soh, Harold},
title={Large Language Models as Zero-Shot Human Models for Human-Robot Interaction},
publisher = {arXiv},
year = {2023} }


If you have questions or comments, please contact Bowen or Harold.


This research is supported by the National Research Foundation Singapore and DSO National Laboratories under the AI Singapore Programme (AISG Award No: AISG2-RP- 2020-017).

Written by

Bowen Zhang