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

arXiv: https://arxiv.org/abs/2512.24829 · HTML: https://mlsanity.com/paper/2512.24829/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 31 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation](https://mlsanity.com/paper/2306.11290/) (2023)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Rearrangement: A Challenge for Embodied AI](https://mlsanity.com/paper/2011.01975/) (2020)
- [Housekeep: Tidying Virtual Households using Commonsense Reasoning](https://mlsanity.com/paper/2205.10712/) (2022)
- [IFOR: Iterative Flow Minimization for Robotic Object Rearrangement](https://mlsanity.com/paper/2202.00732/) (2022)
- [TIDEE: Tidying Up Novel Rooms using Visuo-Semantic Commonsense Priors](https://mlsanity.com/paper/2207.10761/) (2022)
- [My House, My Rules: Learning Tidying Preferences with Graph Neural Networks](https://mlsanity.com/paper/2111.03112/) (2021)
- [Predicting Stable Configurations for Semantic Placement of Novel Objects](https://mlsanity.com/paper/2108.12062/) (2021)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs](https://mlsanity.com/paper/2410.19656/) (2024)
- [A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search](https://mlsanity.com/paper/2206.13396/) (2022)
- [ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenes](https://mlsanity.com/paper/2310.00371/) (2023)
- [SceneScore: Learning a Cost Function for Object Arrangement](https://mlsanity.com/paper/2311.08530/) (2023)
- [DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangement](https://mlsanity.com/paper/2407.08876/) (2024)
- [Personalized Robotic Object Rearrangement from Scene Context](https://mlsanity.com/paper/2505.11108/) (2025)

## 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.