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: 1552256
• 负责人: Aaron Steinfeld
• 金额: $ 10.95万
• 依托单位: Carnegie-Mellon University
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552256&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 将寻求推进科学发展，了解如何最好地将机器人即将发生的故障传达给人类（无论是操作员、主管还是旁观者），从而使人类能够尽可能有效地进行干预以防止故障。该项目有可能显着提高自动机器人和车辆及其周围人类的安全性。 具体目标是发现机器人系统在输送故障方面的设计原则，并确定表达故障的方法，以便人类做出适当的反应。 该研究将重点关注三个用例：远程操作、同地操作和旁观者互动。 为此，该团队将利用各种机器人来支持不同的应用和运动规模。 该团队可用的机器人包括中小型无人地面车辆、人体躯干机器人、机器人轮椅、远程呈现机器人和自动吉普车。 项目成果将通过提高生产力和任务绩效、提高危险职业人员的人身安全以及改善残疾人的生活，影响人机交互领域以及机器人在许多应用领域的未来使用，特别是移动和操纵机器人，包括自动驾驶车辆、工厂机器人和辅助技术。项目负责人的核心研究问题来自于他们之前在任务导向型机器人方面的大量工作。 基于这些经验和其他研究，他们认为以下三个主要因素强烈影响机器人故障期间的用户行为：感知风险（例如，经常崩溃的机器人通常被认为是高风险机器人）、感知严重性（例如，由软材料制成的小型机器人的故障通常被认为不如全身人形机器人严重）和角色（例如，用户是操作员还是旁观者）。 关于这些因素影响失败的方式的尚未探索的研究问题包括。 这些因素（无论是独立的还是组合的）在机器人故障期间如何影响 HRI？ 在机器人故障期间，人类如何利用这些因素？这种利用是否具有很大的可变性，或者人类是否非常一致？ 这些因素将在研究过程中用作自变量，这将推进三个核心领域的知识：制定和验证可概括的定量和定性指标，用于测量人对机器人系统即将发生故障的反应；发现向人类传达故障状态的适当方法；以及初步制定处理故障的通用设计指南。 主要目标是让人们更容易快速理解故障事件并及时采取适当的行动或提供帮助。 PI 特别关注机器人故障的人机交互方面。 因此，他们将跟踪有关诊断故障的文献和研究，但不会为此步骤开发新的系统或概念。 相反，团队将寻求适当且有效的方法来向人类传达故障、故障期间适当的人类响应以及人类行动不可能或不充分时的适当故障状态。