When operating in human environments, robots need to handle complex tasks while both adhering to social norms and accommodating individual preferences. For instance, based on common sense knowledge, a household robot can predict that it should avoid vacuuming during a social gathering, but it may still be uncertain whether it should vacuum before or after having guests. In such cases, integrating common-sense knowledge with human preferences, often conveyed through human explanations, is fundamental yet a challenge for existing systems. In this paper, we introduce GRACE, a novel approach addressing this while generating socially appropriate robot actions. GRACE leverages common sense knowledge from LLMs, and it integrates this knowledge with human explanations through a generative network. The bidirectional structure of GRACE enables robots to refine and enhance LLM predictions by utilizing human explanations and makes robots capable of generating such explanations for human-specified actions. Our evaluations show that integrating human explanations boosts GRACE's performance, where it outperforms several baselines and provides sensible explanations.
View on arXiv@article{dogan2025_2409.16879,
title={ GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations },
author={ Fethiye Irmak Dogan and Umut Ozyurt and Gizem Cinar and Hatice Gunes },
journal={arXiv preprint arXiv:2409.16879},
year={ 2025 }
}