Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences

TL;DR

This paper introduces four interpretable constructs of human object arrangement preferences, validated through a participant study, and demonstrates their integration into robotic planning to produce human-like arrangements.

Contribution

It presents a novel, interpretable preference model for object arrangement, validated by psychological study, and operationalized in a robot planning framework.

Findings

Constructs are psychologically distinct and explanatory.

Preferences guide a Monte Carlo Tree Search planner effectively.

Generated arrangements closely match human preferences.

Abstract

Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning.