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

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[Submitted on 23 Jul 2026]

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

View a PDF of the paper titled StARS: Socially Appropriate Robot Actions via a Recommender System-Driven Approach, by Erencem Ozbey and 3 other authors

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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 this https URL.

Comments: IROS 2026

Subjects:

Robotics (cs.RO); Information Retrieval (cs.IR)

Cite as: arXiv:2607.21802 [cs.RO]

(or arXiv:2607.21802v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.21802

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jin Huang [view email] [v1] Thu, 23 Jul 2026 20:38:07 UTC (447 KB)

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