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.

翻译：用于家居物品重排的机器人系统通常依赖于从人类演示中推断出的潜在偏好模型。尽管这些模型在预测方面表现有效，但它们对指导人类决策的可解释因素提供的洞察有限。我们提出了一个沿四个可解释构念的物体排列偏好的显式表述：空间实用性（将物品置于其在空间中自然最合适的位置）、习惯便利性（使常用物品易于取用）、语义连贯性（将用于相同任务或在语境上相关的物品放置在一起）以及常识适当性（将物品置于人们通常期望找到它们的地方）。为了捕捉这些构念，我们通过一项涉及63名参与者的在线研究设计并验证了一份自我报告问卷。结果证实了这些构念在心理上的独特性及其在两种场景（厨房和客厅）中的解释力。我们通过将这些构念集成到蒙特卡洛树搜索（MCTS）规划器中来展示其实用性，并证明当以参与者衍生的偏好为指导时，我们的规划器能够生成合理的排列，这些排列与参与者生成的排列高度一致。这项工作贡献了一个紧凑、可解释的物体排列偏好表述，并展示了如何将其操作化以用于机器人规划。