用心理模型量化机器人对操作员的信任，实时评估人机协作可靠性。
The ATTUNE model for Artificial Trust Towards Human Operators
- 基于心智理论构建信任评估框架，融合操作员状态与意图信息。
- 在灾难救援模拟场景中验证，实现对人类信任的实时量化。
- 适合人机协作、智能机器人领域研究者参考。
本文提出一种新方法以量化人机交互中的信任。构建了一个针对特定任务的HRI框架，用于实时估算机器人对人类操作员的信任度。该框架基于心智理论原则，整合了操作员的状态、行为与意图信息，建立了名为ATTUNE（Artificial Trust Towards Human Operators）的人工信任模型。模型通过操作员的注意力状态、导航意图、动作及表现等指标来量化信任水平。在包含仿真灾难响应场景下人类操作记录（ROSbags）的现有数据集上测试了ATTUNE性能，通过定性与定量分析评估其效果，结果为后续研究提供了洞见并有助于优化该方法。
原文摘要 · Abstract (English)
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.
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