让机器人根据用户记忆动态调整解释细节，提升长期交互理解度。
Personalised Explanations in Long-term Human-Robot Interactions
- 基于用户知识记忆模型，动态更新解释内容
- 两阶段架构有效减少无关信息，仅在有相关记忆时简化说明
- 适用于医院巡逻与厨房助手等长期人机协作场景
在人机交互（HRI）领域，提升人类对机器人的理解是核心挑战。新兴的可解释人机交互（XHRI）研究致力于生成解释并评估其对交互的影响。以往工作强调需个性化解释的详细程度以增强可用性与理解度。本文提出一种框架，用于更新和检索用户知识-记忆模型，从而在参考已有概念的基础上调整解释的详细程度。基于该框架的三种大语言模型（LLMs）架构在两个场景中进行评估：医院巡检机器人与厨房助手机器人。实验结果表明，采用两阶段架构——先生成解释再个性化处理——能有效仅在用户存在相关知识时降低解释细节，显著提升交互效率。
原文摘要 · Abstract (English)
In the field of Human-Robot Interaction (HRI), a fundamental challenge is to facilitate human understanding of robots. The emerging domain of eXplainable HRI (XHRI) investigates methods to generate explanations and evaluate their impact on human-robot interactions. Previous works have highlighted the need to personalise the level of detail of these explanations to enhance usability and comprehension. Our paper presents a framework designed to update and retrieve user knowledge-memory models, allowing for adapting the explanations' level of detail while referencing previously acquired concepts. Three architectures based on our proposed framework that use Large Language Models (LLMs) are evaluated in two distinct scenarios: a hospital patrolling robot and a kitchen assistant robot. Experimental results demonstrate that a two-stage architecture, which first generates an explanation and then personalises it, is the framework architecture that effectively reduces the level of detail only when there is related user knowledge.
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