发表机构
The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究类人机器人设计中恐怖谷效应，提出分层贝叶斯生成模型，将相关准则转化为数学变量，通过模拟和实验验证，该模型能将经验启发式方法转变为计算基础，用于评估和优化机器人外观与行为。
AI 中文摘要
恐怖谷效应是类人机器人设计中一个长期存在的经验法则：使机器人更像人类可能会降低而非增加亲和力。现有准则难以用于算法设计，因为它们未表示为可操作变量。本文提出了一种分层贝叶斯生成模型，将这些准则作为数学设计变量进行操作。该模型将对类人机器人的亲和力表示为后验加权负类别条件惊奇，并将类别模糊性和感知不匹配解释为惊奇的增加。它将恐怖谷机制映射到四个变量上。模拟表明类别模糊性和外观 - 运动不匹配会降低亲和力，不确定性会重塑恐怖谷。在一项人类主体实验中，通过模糊先验机器人刺激操纵预测不确定性，通过模糊评估刺激操纵观察不确定性。增加的观察不确定性减弱了中等人类相似度下熟悉度评分的下降，而低预测不确定性增加了对类机器人外观的评分。该框架将经验性的恐怖谷启发式方法转变为算法评估和优化类人机器人外观及行为的计算基础。
英文摘要
The uncanny valley is a long-standing empirical rule in humanoid robot design: making robots more human-like can reduce, rather than increase, affinity. Yet existing guidelines, such as adopting robot-like appearances, avoiding excessive realism, and reducing cross-modal mismatches, remain difficult to use for algorithmic design because they are not expressed as manipulable variables. Here, we propose a hierarchical Bayesian generative model that operationalizes these guidelines as mathematical design variables. The model represents affinity toward humanoid robots as posterior-weighted negative category-conditional surprise and explains category ambiguity and perceptual mismatch as increases in surprise. It maps uncanny-valley mechanisms onto four variables: deviation from the predicted robot-category mean, inconsistency in human likeness across modalities, prediction uncertainty, and observational uncertainty. Simulations showed that category ambiguity and appearance--motion mismatch can produce affinity reductions, and that uncertainty reshapes the valley. In a human-subject experiment with robot--human morphing images, we manipulated prediction uncertainty using blurred prior robot stimuli and observational uncertainty using blurred evaluation stimuli. Increased observational uncertainty attenuated the decrease in familiarity ratings at intermediate human likeness, whereas low prediction uncertainty increased ratings for robot-like appearances. This framework turns empirical uncanny-valley heuristics into a computational basis for algorithmically evaluating and optimizing humanoid robot appearance and behavior.
