发表机构
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有社会主动智能研究忽视个体差异的问题，提出个性化基准RobotEQ 3.0，通过用户画像与偏好预测，利用个体特质作为输入，实现面向特定用户的具身智能体行为预测，推动从平均用户到个性化系统的范式转变。
AI 中文摘要
社会主动智能（SPI）是一个新兴的研究领域，旨在将具身智能体从被动辅助转向主动理解人类需求并执行符合社会期望的行为。先前的工作主要集中于普通用户。然而，人类的期望本质上是多样化的，先前的工作忽视了个体差异。为弥合这一差距，我们引入了RobotEQ 3.0，一个用于个性化SPI的基准测试。（数据集）我们首先通过结构化问卷对参与者进行画像，问卷涵盖与人类对具身智能体期望相关的因素，如基本人口统计信息和人格特质。参与者随后从一组候选项中选择他们偏好的行动。与先前侧重于评估行为适当性的SPI基准不同，我们的任务聚焦于预测特定用户偏好的行动，从而捕捉人类的主观性。所得数据集建立了个体特质与行为偏好之间的明确联系。（解决方案）我们观察到显著的标注者间差异，证实用户对行动的偏好高度个性化。这促使我们探索个性化SPI，其中用户特质作为额外输入以预测个体偏好。实验结果表明，纳入用户特质有助于个性化预测。这项工作旨在将研究范式从开发适合普通用户的智能体转向设计针对特定个体的系统。
英文摘要
Social Proactive Intelligence (SPI) is an emerging research area, aiming to shift embodied agents from reactive assistance toward proactively understanding human needs and executing socially desirable actions. Prior work has largely centered on the average user. However, human expectations are inherently diverse, and prior work overlooks individual nuances. To bridge this gap, we introduce RobotEQ 3.0, a benchmark for Personalized SPI. (Dataset) We first profile participants via a structured questionnaire covering factors that are correlated with human expectations of embodied agents, such as basic demographics and personality traits. Participants then select their preferred actions from a set of candidates. Unlike prior SPI benchmarks that focus on assessing behavioral appropriateness, our task centers on predicting the actions preferred by a specific user, thereby capturing human subjectivity. The resulting dataset establishes explicit links between individual traits and behavioral preferences. (Solution) We observe substantial inter-annotator variance, confirming that user preferences over actions are highly individualized. This motivates our exploration of Personalized SPI, in which user traits serve as additional inputs to predict individual preferences. Experimental results show that incorporating user traits can aid personalized prediction. This work aims to shift the research paradigm from developing agents suited for the average user to designing systems tailored to specific individuals.
