用大模型+人类解释生成符合社交规范的机器人动作
GRACE: Generating Socially Appropriate Robot Actions Leveraging LLMs and Human Explanations
- 结合大模型常识与人类解释，双向优化动作决策
- 在真实场景中显著提升动作合理性，优于多个基线方法
- 适合需要理解社会规范的家用机器人研发者
在人类环境中运行的机器人需在遵守社交规范的同时适应个体偏好。例如，基于常识，家庭机器人可预测应避免在聚会期间吸尘，但对是否应在访客前来前或后吸尘仍存疑。此时，将常识知识与通过人类解释传达的偏好融合至关重要，却是现有系统面临的挑战。本文提出GRACE，一种新方法，通过大语言模型获取常识知识，并利用生成网络将其与人类解释相结合。GRACE的双向结构使机器人能借助人类解释优化大模型预测，同时能为人类指定的动作生成合理解释。评估表明，融入人类解释显著提升了GRACE性能，其表现优于多个基线，并能生成合乎逻辑的解释。
原文摘要 · Abstract (English)
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.
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