This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach.

翻译：本文提出了一种量化人机交互中信任度的新方法。针对特定限定任务场景，该研究构建了一个用于评估机器人对人类信任度的人机交互框架。该框架能够实时估计与移动遥操作机器人交互过程中，人工智能体对人类合作伙伴的人工信任水平。该框架的设计基于心智理论原则，整合了人类状态、行为及意图等多维度信息。基于此框架，我们构建了面向人类操作者的人工信任ATTUNE模型。该模型通过操作者的注意力状态、导航意图、操作行为及任务表现等指标，实现对人类信任度的量化评估。研究采用包含模拟灾难响应场景人类试验记录（ROS数据包）的既有数据集对模型进行验证，并通过定性与定量分析评估ATTUNE模型的性能。分析结果明确了后续研究方向，并为方法改进提供了依据。