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

Q: What problem does the paper address for service robots searching household objects?

It addresses the dilemma of whether and when to personalize search locations based on resident traits, given that object placement can be stable for some items but variable for others.

Q: How does PerSim decide when to personalize?

PerSim uses a rigidity-gated hybrid policy that mixes a trait-conditioned prior with a population-frequency baseline, enabling personalization primarily for objects whose placement behavior is variable.

Q: How is PerSim trained and validated without large invasive longitudinal datasets?

The method uses a human-calibrated simulation pipeline to generate and validate object-placement transitions across diverse home layouts, then trains a predictor using continuous Big Five vectors to produce room-level and within-room co-occurrence cues, validated through human studies and objective tests.

Jul 16, 2026

Upload by Emma Wilson

Service robots retrieving household objects often depend on spatial priors to cut search cost, but object placement varies across residents. Collecting longitudinal, trait-specific trajectories is invasive and difficult to scale, leaving uncertainty about when personalization is truly helpful. This work proposes PerSim, a rigidity-gated hybrid policy that personalizes only when placement behavior is variable, combining trait-conditioned priors with a population-frequency baseline. A human-calibrated simulation pipeline and Big Five–conditioned prediction yield decision rules and end-to-end search-cost reductions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

Introduction

Problem and motivation: when personalization helps

Data bottleneck and limitations

Proposed approach: PerSim and rigidity gating

Evaluation approach and expected outcomes

When to Personalize Household Object Search: A Rigidity-Gated Hybrid Policy Xianyao Li 1 , Yuhai Wang2 , Hu Xiao2 , Kaleb Smith3 , Gilbert Yang Ye2 and Eric Jing Du 1 arXiv :2607 .00022v2 [ cs .RO] 2 Jul 2026 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 rigiditygated 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 cooccurrence 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 < 10−6), and (ii) in a blinded A/B comparison, personalization is favored primarily for lowrigidity 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 fivedimensional 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. I. INTRODUCTION In everyday homes, small items go missing constantly: a mug gets left in the bedroom, a phone slips between couch cushions, or keys end up on an unexpected surface. Home service robots must therefore search intelligently—when asked to fetch a mug, phone, or keys, a robot should prioritize likely locations rather than exhaustively scanning every room [1]–[3] . Spatial priors make this possible. Yet household object locations are not determined by environment semantics alone (e.g., mugs near kitchens); they are also shaped by resident traits. Some residents consistently return items to fixed storage, while others tolerate functional clutter and frequent relocation. This creates a practical dilemma for robotics: personalization can help—but not always. If a robot personalizes aggressively when behavior is stable and universally shared, it risks overfitting noise; if it never personalizes when behavior is variable and residentdependent, it wastes an opportunity to reduce search cost. 1X. Li and E. J. Du are with the Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, [USA.](USA. xianyao.li@ufl.edu)[ xianyao.li@ufl.edu](USA. xianyao.li@ufl.edu), [eric.du@essie.ufl.edu](eric.du@essie.ufl.edu). 2Y. Wang, H. Xiao, and G. Y. Ye are with the Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA. 3 K. Smith is with NVIDIA, [kasmith@nvidia.com](kasmith@nvidia.com. Project)[. Project](kasmith@nvidia.com. Project)[ ](kasmith@nvidia.com. Project)resources: [https://github.com/XianyaoLi/PerSim](https://github.com/XianyaoLi/PerSim). Fig. 1. PerSim as a hypothesis-driven framework for rigidity-gated personalization. (1) Human anchors provide resident profiles and objectlevel placement/rigidity signals to calibrate a constrained generative model, producing behaviorally plausible synthetic dynamics (validated by L1) . (2) A clean predictor learns trait-conditioned room priors and cue priors for twostage search (stage 1: room ranking; stage 2: within-room cueing), whose outputs are validated by a blinded preference study (L2) . (3) A rigidity-gated hybrid po

FAQs

What problem does the paper address for service robots searching household objects?

It addresses the dilemma of whether and when to personalize search locations based on resident traits, given that object placement can be stable for some items but variable for others.

How does PerSim decide when to personalize?

PerSim uses a rigidity-gated hybrid policy that mixes a trait-conditioned prior with a population-frequency baseline, enabling personalization primarily for objects whose placement behavior is variable.

How is PerSim trained and validated without large invasive longitudinal datasets?

The method uses a human-calibrated simulation pipeline to generate and validate object-placement transitions across diverse home layouts, then trains a predictor using continuous Big Five vectors to produce room-level and within-room co-occurrence cues, validated through human studies and objective tests.

Jul 16, 2026

Upload by Emma Wilson