Problem
Research questions and friction points this paper is trying to address.
Social Proactive Intelligence
Embodied Agents
Personalization
User Preferences
Individual Differences
RobotEQ 3.0: Towards Personalized Social Proactive Intelligence in Embodied Agents
🤖 AI Summary
This study addresses the limitation of embodied agents that overlook individual user differences by being designed solely for an "average user," and proposes a personalized, socially proactive intelligence benchmark. Methodologically, it establishes trait-behavior associations through structured questionnaires and user profile modeling, shifting the evaluation paradigm from assessing general behavioral appropriateness to predicting specific user preferences in order to capture the inherent subjectivity of human interaction. The findings demonstrate that user preferences are highly individualized, and incorporating user trait modeling significantly enhances preference prediction accuracy. This work thereby provides a novel paradigm for personalized interaction in embodied artificial intelligence.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Personalized Social Proactive Intelligence
Embodied Agents
User Profiling
Behavioral Preference Prediction
Benchmark
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