StARS: Socially Appropriate Robot Actions via a Recommender System-Driven Approach

2026-07-23

AI Summary

This work addresses the challenge that socially appropriate robot behaviors in human–robot interaction vary across individuals by proposing the StARS framework, which introduces recommender system principles to this domain for the first time. Treating annotators as users, interaction contexts as items, and appropriateness ratings of robot actions as preference signals, StARS combines collaborative filtering with learnable context representations to model personalized social norms. The framework is model-agnostic, compatible with diverse backbone architectures and context encoding strategies, and robust to sparse feedback. Evaluated on the MannersDB+ and SocNav1 datasets, StARS significantly improves both action recommendation performance and alignment with individual user ratings, effectively enabling the generation of robot behaviors tailored to personal social expectations.

Abstract

Social appropriateness in human-robot interaction (HRI) is not universal: different people can judge the same robot action differently in the same situation. To capture this inter-subject variability, we reformulate socially appropriate action generation as a preference modelling problem inspired by recommender systems, treating annotators as users, contexts/scenes as items, and appropriateness scores over a set of candidate robot actions as targets. We propose StARS, a novel model-agnostic framework that integrates collaborative filtering with learnable scene representations to generate user-specific appropriateness scores over candidate robot actions. StARS is model-agnostic: it can be integrated with various scene encoders and backbones, enabling personalisation without redesigning the underlying model. We evaluate StARS on two socially aware robotics datasets, MannersDB+ and SocNav1, and analyse robustness under sparse preference feedback. Across datasets and backbones, StARS consistently improves performance and agreement with annotators, supporting personalised action selection aligned with user norms. Our code is publicly available at https://github.com/Cambridge-AFAR/StARS.git.

Problem

• social appropriateness
• human-robot interaction
• preference modelling
• personalisation
• inter-subject variability

Innovation

• socially appropriate robot actions
• recommender system
• preference modelling
• personalisation
• collaborative filtering