用对话AI让机器人解释决策，提升人机协作理解力。
Improving robot understanding using conversational AI: demonstration and feasibility study
- 设计四级解释机制，按需生成可懂说明。
- 用户实测显示交互可行，错误场景下理解度提升。
- 适合人机协作、智能服务等需要透明决策的场景。
解释是实现成功人机交互的重要环节，有助于提升机器人理解能力。为改善机器人理解，本文基于两个核心问题（需解释什么、为何做出特定决策）构建了四级解释层级（LOE）。当人类对机器人的认知模型与机器人自身状态存在差异时，系统会触发沟通行为，通过对话式AI平台生成解释内容。采用自适应对话机制实现不同解释层级间的平滑过渡。研究在包含错误的协作任务中演示了该机制，并开展了用户可行性研究，验证了其实际应用潜力。
原文摘要 · Abstract (English)
Explanations constitute an important aspect of successful human robot interactions and can enhance robot understanding. To improve the understanding of the robot, we have developed four levels of explanation (LOE) based on two questions: what needs to be explained, and why the robot has made a particular decision. The understandable robot requires a communicative action when there is disparity between the human s mental model of the robot and the robots state of mind. This communicative action was generated by utilizing a conversational AI platform to generate explanations. An adaptive dialog was implemented for transition from one LOE to another. Here, we demonstrate the adaptive dialog in a collaborative task with errors and provide results of a feasibility study with users.
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