未被遗忘：人形机器人头部Kim的个性化情景记忆的实现与评估
Not Forgotten: Implementation and Evaluation of a Personalized Episodic Memory for the Humanoid Robot Head Kim
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中文总结 AI 辅助
研究针对社交机器人跨会话无记忆问题，提出在人形机器人头部Kim上集成轻量级情景记忆模块，结合语义检索与对话系统，用混合评分函数检索交互并注入提示，实验表明该模块提升社交性且未引发负面反应。
中文摘要 AI 辅助
依赖大语言模型进行对话的社交机器人无法跨会话保留信息。这种记忆缺失违反了社会期望，可能阻碍持久关系的形成。本文提出了一种轻量级情景记忆模块，将基于向量的语义检索与大语言模型控制的对话系统集成，部署在人形机器人头部Kim上。该模块采用结合余弦相似度与记忆强度度量的混合评分函数来检索上下文相关的过去交互并将其注入生成提示中。系统在一项基于视频的在线内组研究（N = 43）中使用人机交互评估量表（HRIES）进行了评估。结果表明，情景记忆显著提高了感知到的社交性（d = 0.60，p <.001），对感知到的可信度（d = 0.62）和温暖度（d = 0.56）影响最强。感知到的干扰保持不变（d = 0.00），表明所实施的个性化回忆方法未引发与隐私相关的不适或恐怖谷效应。这些发现表明，情景记忆在具身人机交互中起到社交润滑剂的作用，提高关系质量而不引发负面情感反应。
英文摘要
Social robots that rely on large language models for conversation are unable to retain information across sessions. This absence of memory violates social expectations, potentially preventing the formation of persistent relationships. This paper presents a lightweight episodic memory module that integrates vector-based semantic retrieval with an LLM-controlled dialog system, deployed on the humanoid robot head Kim. The module employs a hybrid scoring function combining cosine similarity with a memory strength metric to retrieve contextually relevant past interactions and inject them into the generation prompt. The system was evaluated in a within-subjects video-based online study (N = 43) using the Human-Robot Interaction Evaluation Scale (HRIES). Results show that episodic memory significantly increased perceived sociability (d = 0.60, p < .001), with the strongest effects on perceived trustworthiness (d = 0.62) and warmth (d = 0.56). Perceived disturbance remained unchanged (d = 0.00), indicating that the implemented approach to personalized recall did not trigger privacy-related discomfort or uncanny valley effects. These findings suggest that episodic memory serves as a social lubricant in embodied Human-Robot Interaction, enhancing relational quality without eliciting negative affective responses.
发表机构
- Stuttgart Media University（斯图加特媒体大学）
- University of Lübeck（吕贝克大学）
机构由 AI 辅助整理，请以论文原文为准。
