GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations
2025 IEEE International Conference on Robotics and Automation, ICRA 2025, Georgia, United States Of America, 19 - 23 May 2025, pp.4330-4336, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/icra55743.2025.11127826
- City: Georgia
- Country: United States Of America
- Page Numbers: pp.4330-4336
- Middle East Technical University Affiliated: Yes
Abstract
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 pre-dict 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.