EAGER: Collaborative Research: Exploring Models for Conveying Imminent Robot Failures to Allow for Human Intervention

EAGER：协作研究：探索传达即将发生的机器人故障以允许人类干预的模型

• 批准号: 1552228
• 负责人: Holly Yanco
• 金额: $ 18.47万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552228&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Exploring; Models

In this exploratory research, the PIs will seek to advance the state of the science on how best to convey a robot's imminent failure to a human (whether an operator, supervisor, or bystander), in a manner that could allow the human to intervene as effectively as possible to prevent the failure. This project has the potential to dramatically increase the safety of humans in and around autonomous robots and vehicles. Specific goals are to discover design principles for robot systems with respect to conveying failure, and to identify methods for expressing failure so that humans react appropriately. The research will focus on three use cases: remote operation, co-located operation, and bystander interaction. To these ends, the team will utilize a variety of robots in order to support different applications and movement scales. Robots available to the team include small and mid-size unmanned ground vehicles, human-scale torso robots, a robot wheelchair, a telepresence robot, and an autonomous Jeep. Project outcomes will impact the field of human-robot interaction and the future use of robots in many application domains, particularly those of mobile and manipulation robots, including autonomous vehicles, factory robots, and assistive technology, by enhancing productivity and task performance, increasing personal safety for those who work in hazardous occupations, and improving the lives of persons with disabilities.The PIs' core research questions are informed by their substantial prior work with task-oriented robots. Based on that experience and other studies, they argue that the following three main factors strongly influence user actions during robot failure: perceived risk (e.g., a robot that crashes frequently is generally perceived as a high risk robot), perceived severity (e.g., the failure of a small robot made of soft materials is generally perceived as less severe than that of a full body humanoid robot), and role (e.g., is the user an operators or a bystander). Unexplored research questions about the manner in which these factors impact failure include. How do these factors, both independently and in combination, influence HRI during robot failures? How do humans utilize these factors during robot failure, and does this utilization have high variability or are humans very consistent? These factors will be used as independent variables during studies which will advance knowledge in three core areas: formulation and validation of generalizable quantitative and qualitative metrics for measuring a person's response to an imminent failure in a robot system; discovery of appropriate ways to communicate failure states to humans; and initial development of common design guidelines for handling failures. The primary goal is to make it easier for humans to rapidly understand failure events and to act or assist appropriately in a timely manner. The PIs are specifically focused on the human-robot interaction aspect of robot failures. As such, they will track literature and research on diagnosing failures, but will not develop new systems or concepts for this step. Instead, the team will seek appropriate and effective ways to convey failures to humans, appropriate human responses during failures, and appropriate failure states when human action is not possible or is insufficient.

在这项探索性研究中，PI将寻求在如何最好地传达机器人迫在眉睫的失败给人类的科学状态（无论是操作员，主管还是旁观者），以使人类能够尽可能有效地干预以防止失败的方式进行干预。该项目有可能大大提高人类在自动机器人和车辆周围的安全性。 具体的目标是在传达失败方面发现机器人系统的设计原理，并确定表达失败的方法，以便人类做出适当的反应。 该研究将重点介绍三种用例：远程操作，共同置换的操作和旁观者的互动。 对于这些目的，团队将利用各种机器人来支持不同的应用程序和运动量表。 该团队可用的机器人包括中小型无人接地车，人体规模的躯干机器人，机器人轮椅，远程敏感机器人和自动吉普车。 项目成果将通过在许多应用领域（尤其是移动和操纵机器人）在包括自动驾驶汽车，工厂机器人和辅助技术的包括移动和操纵机器人中的机器人中的未来使用，通过提高生产力和任务绩效，增加从事危险职业的人的个人安全，并通过核心研究人员进行核心问题。 基于该经验和其他研究，他们认为以下三个主要因素强烈影响机器人失败期间用户的行动：感知的风险（例如，经常被撞车的机器人通常被视为高风险机器人），被感知的严重性（例如，由软材料制成的小机器人的失败通常比起全身人体机器人或e。 关于这些因素影响失败的方式的未开发的研究问题。 这些因素如何独立和组合影响机器人失败期间的HRI？ 人类在机器人故障期间如何利用这些因素，这种利用情况是否具有很高的可变性或人类非常一致？ 这些因素将在研究过程中用作自变量，这些变量将在三个核心领域中提高知识：公理定量和定性指标的制定和验证，以衡量一个人对机器人系统中迫在眉睫的失败的反应；发现适当的方式将失败状态传达给人类；并初步制定了处理故障的常见设计指南。 主要目标是使人类更容易迅速了解失败事件，并及时采取行动或适当协助。 PI专门针对机器人故障的人类机器人相互作用方面。 因此，他们将跟踪有关诊断失败的文献和研究，但不会为此步骤开发新的系统或概念。 取而代之的是，团队将寻求适当有效的方法向人类传达失败，在失败期间的适当人类反应以及当人类行动不可能或不足时的适当失败状态。