When to Personalize Household Object Search: A Rigidity-Gated Hybrid Policy
Service robots searching for household objects rely on spatial priors to reduce search cost, yet object locations can vary with resident traits. This paper proposes PerSim, a rigidity-gated hybrid policy that combines a trait-conditioned prior with a population-frequency baseline, personalizing only when placement behavior is variable. Through a human-calibrated simulation pipeline and a unified human study (N=200), they show that personalization is favored primarily for low-rigidity objects, while the population-frequency baseline remains strong for universally placed items. Offline tests show improvement over nearest discrete configuration matching, and home digital twin experiments demonstrate reduced expected search cost.
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[Submitted on 18 Jun 2026]
Title:When to Personalize Household Object Search: A Rigidity-Gated Hybrid Policy
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Abstract:Service robots searching for household objects rely on spatial priors to reduce search cost, yet object locations can vary with resident traits. Collecting longitudinal, trait-specific in-home trajectories is invasive and hard to scale. We study when personalization helps and propose PerSim, a rigidity-gated hybrid policy that combines a trait-conditioned prior with a population-frequency baseline, personalizing only when placement behavior is variable. To scale resident-conditioned dynamics, we employ a human-calibrated simulation pipeline to generate and validate object-placement transitions in diverse home layouts, and train a predictor that injects continuous Big Five vectors to output room-level priors and within-room co-occurrence cues. In a unified human study (N=200), dual-layer validation shows that (i) synthetic transitions are judged behaviorally plausible (mean 3.85/5, p
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