GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations
<|reference_start|>GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations: 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 Large Language Models (LLMs), and it integrates this knowledge with human explanations through a generative network architecture. 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 experimental evaluations show that integrating human explanations boosts GRACE's performance, where it outperforms several baselines and provides sensible explanations.<|reference_end|>
@article{dogan2024grace:,
    title={GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and
  Human Explanations},
    author={Fethiye Irmak Dogan, Umut Ozyurt, Gizem Cinar and Hatice Gunes},
    journal={arXiv preprint arXiv:2409.16879},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16879},
    primaryClass={cs.RO}
}