HCC: Medium: Collaborative Research: Development of Trust Models and Metrics for Human-Robot Interaction

HCC：媒介：协作研究：人机交互信任模型和指标的开发

• 批准号: 0905228
• 负责人: Holly Yanco
• 金额: $ 50万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905228&HistoricalAwards=false
• 关键词: HCC; Medium; Collaborative; Research; Development

It is often assumed that the use of robots to help people execute tasks will result in better performance than if the person or robot were operating alone. However, research in automated systems suggests that the performance of a human-machine system depends on the extent to which the person trusts the machine and the extent to which this trust (or distrust) is justified. As robots are being developed to aid people with complex tasks, it is critical not only that we build systems which people can trust, but that these systems also foster an appropriate level of trust based on the capabilities of the systems. A user who does not have an appropriate level of trust in the robot may misuse or abuse the robot's autonomous capabilities or expose people to danger. This project proposes to develop quantitative metrics to measure a user's trust in a robot as well as a model to estimate the user's level of trust in real time. Using this information, the robot will be able to adjust its interaction accordingly. Promoting appropriate levels of trust will be particularly beneficial in safety-critical domains such as urban search and rescue and assistive robotics, in which users risk harm to themselves, the robot, or the environment if users do not trust the robot enough to rely on its autonomous capabilities. The research has the potential for a large impact on the field of human-robot interaction as few studies have explicitly examined issues involving trust of robots. Being able to model trust and foster appropriate levels of trust will result in more effective use of robotic automation, safer interactions, and better task performance.

人们通常认为，使用机器人来帮助人们执行任务将导致比人或机器人单独操作更好的性能。然而，自动化系统的研究表明，人机系统的性能取决于人对机器的信任程度以及这种信任（或不信任）的合理程度。 随着机器人被开发用于帮助人们完成复杂的任务，我们不仅要构建人们可以信任的系统，而且这些系统还要根据系统的能力培养适当的信任水平。 对机器人没有适当信任程度的用户可能会误用或滥用机器人的自主能力，或使人们面临危险。 该项目建议开发定量指标来衡量用户的信任机器人以及模型来估计用户的信任水平在真实的时间。 使用这些信息，机器人将能够相应地调整其交互。在城市搜索和救援以及辅助机器人等安全关键领域，促进适当的信任水平将特别有益，在这些领域，如果用户不足够信任机器人以依赖其自主能力，则用户可能会对自己、机器人或环境造成伤害。 这项研究有可能对人机交互领域产生重大影响，因为很少有研究明确研究涉及机器人信任的问题。 能够对信任进行建模并培养适当的信任水平将导致更有效地使用机器人自动化，更安全的交互和更好的任务性能。