Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 61 references that are in the map. First posted on arXiv 31 Dec 2025.
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References · 16
- 2023TidyBot: Personalized Robot Assistance with Large Language Modelscited by 219
- 2023Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigationcited by 162
- 2021Monte Carlo Tree Search: A Review of Recent Modifications and Applicationscited by 158
- 2020Rearrangement: A Challenge for Embodied AIcited by 153
- 2022Housekeep: Tidying Virtual Households using Commonsense Reasoningcited by 57
- 2022IFOR: Iterative Flow Minimization for Robotic Object Rearrangementcited by 42
- 2022TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priorscited by 30
- 2021My House, My Rules: Learning Tidying Preferences with Graph Neural Networkscited by 27
- 2021Predicting Stable Configurations for Semantic Placement of Novel Objectscited by 23
- 2021Explainable Autonomous Robots: A Survey and Perspectivecited by 15
- 2024APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMscited by 11
- 2022A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Searchcited by 6
4 more
- 2023ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenescited by 6
- 2023SceneScore: Learning a Cost Function for Object Arrangementcited by 4
- 2024DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangementcited by 3
- 2025Personalized Robotic Object Rearrangement from Scene Contextcited by 1
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