This repository contains the supplementary material for our IROS 2023 paper Large Language Models as Zero-Shot Human Models for Human-Robot Interaction.
@inproceedings{zhang2023large,
title={Large language models as zero-shot human models for human-robot interaction},
author={Zhang, Bowen and Soh, Harold},
booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={7961--7968},
year={2023},
organization={IEEE}
}
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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. In this work, we explore the potential of large-language models (LLMs) to act as zero-shot human models for HRI.
Here is a high-level illustration of how LLM-based human models can be used for planning in HRI:
Our experiments on three social datasets yield promising results; the LLMs are able to achieve performance comparable to purpose-built models. 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. 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.
The experiments on three social datsets: MANNERS, SocialIQA and Trust Transfer and their results are in the respective folders. Each folder consists of a utility file and a Jupyter notebook that contains the instructions to reproduce the experiment results and evaluation results.
The case studies are stored in table_clearing and utensil_passing respectively. The utensil_passing folder also contains the crowdsourced results from questionnaires.
