arXiv:2512.24829cs.AIcs.HC2025-12中稿 · the 2026 ACM/IEEE …

提出4类可解释的物品摆放偏好,让机器人理解人类为何把东西放哪儿。

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

  • 从人类行为中提炼出空间实用、习惯便利、语义关联和常识适宜四类摆放原则。
  • 63人问卷验证这些原则在厨房与客厅场景中具有显著区分性和解释力。
  • 将这些原则融入规划算法,生成结果更贴近人类实际摆放习惯。

家用机器人进行物品重排常依赖从人类示范中学习的隐式偏好模型,虽预测有效,但缺乏对人类决策依据的可解释性。本文提出四种可解释的物品摆放偏好:空间实用性(放物品于最自然的位置)、习惯便利性(高频使用物品易取用)、语义一致性(功能相关物品集中放置)和常识适宜性(符合常规预期)。通过63名参与者在线问卷研究,验证了这四类偏好的心理独立性及其在厨房和客厅场景中的解释能力。将参与者偏好数据融入蒙特卡洛树搜索(MCTS)规划器后,生成的物品布局与人类实际摆放高度一致。本工作提供了一种紧凑且可解释的摆放偏好表达方式,并展示了其在机器人规划中的可操作性。

原文摘要 · Abstract (English)

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.

可解释性机器人规划人类偏好

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