When to Personalize Household Object Search: A Rigidity-Gated Hybrid Policy

2026-06-18

AI Summary

This work addresses the challenge of enabling service robots to determine when to leverage user personality for object search in home environments, balancing privacy preservation and scalability. The authors propose PerSim, a novel strategy featuring a “rigid gating” mechanism that activates personalization only when variability in object placement behavior is high. For the first time, continuous Big Five personality traits are integrated into room-level spatial priors and indoor co-occurrence cues to predict object locations. A human-calibrated, scalable simulation framework is developed to generate diverse household object trajectories. Experiments demonstrate that personalization significantly improves search performance for low-rigidity objects (p=0.005), achieves a behavioral plausibility score of 3.85/5, outperforms discrete personality matching on unseen continuous trait vectors (p=0.035), and effectively reduces overall search cost in digital twin households.

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<1e-6), and (ii) in a blinded A/B comparison, personalization is favored primarily for low-rigidity objects (p=0.005), while the population-frequency baseline remains strong for universally placed items, yielding a decision rule for when to personalize. In an offline objective test, we observe a small but significant improvement on unseen continuous trait vectors over nearest discrete configuration matching (p=0.035), supporting interpolation in five-dimensional trait space. Finally, in a home digital twin we show that PerSim reduces expected search cost by combining room visitation effort with within-room cue checking, demonstrating end-to-end gains beyond isolated prediction metrics.

Problem

• personalization
• household object search
• spatial priors
• resident traits
• service robots

Innovation

• personalization
• rigidity-gated policy
• human-calibrated simulation
• Big Five traits
• object search