EAGER: Evidence-Based Model of Adoption of Robotics for Pandemics and Natural Disasters

EAGER：采用机器人技术应对流行病和自然灾害的循证模型

• 批准号: 2125988
• 负责人: Robin Murphy
• 金额: $ 23.83万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125988&HistoricalAwards=false
• 关键词: EAGER; Evidence; Based; Model; Adoption

Robotics innovations (unmanned ground, aerial, and marine systems) have been sporadically used for disaster response by emergency management agencies since 2001. Disasters pose a dilemma: on one hand, there is often an emotional urge to try anything in the hopes of coping with overwhelming potential loss of life and livelihoods; on the other hand, the poorly considered introduction of a robot into a disaster can lead to worse outcomes than doing nothing. This EArly Concept Grant for Exploratory Research (EAGER) study will bring together leaders in robotics, law, emergency management, and public health with expertise in emergency management and policy to investigate robotics innovations and instances of ethical concerns during the COVID-19 pandemic. The result will be the first theory of responsible robotics innovation for disasters. This will transform how responders select robot technology during a disaster, ultimately saving lives, mitigating long-term environmental and health impacts, and accelerating economic recovery. The project will provide evidence for anticipatory governance, such as new regulations and policies, to accelerate the adoption of safe, effective robots during a disaster while reducing negative consequences from either deploying unsound technology or deferring deployment of technology. The project will impact how engineering, law, and policy is taught, train graduate and undergraduate students in multidisciplinary approaches to science and society, and increase the diversity of students in the research pipeline. The multidisciplinary team will conduct a rigorous analysis, featuring structured interviews with clinical healthcare providers, public health and public safety officials worldwide who deployed robots during the pandemic in order to understand the influences on adoption. The demand analysis will be complemented by prior work in quantitatively classifying the capabilities of the robot for a use case; together these orthogonal sets of user-centric and robot-centric influences will create a novel template for describing future innovation. The project will explore the legal systems and how they adapted to the exigencies of the pandemic, especially any correlations with national policies on robotics, and investigate emergent ethical concerns. The resulting quantitative model is expected to be both prescriptive for policy and predictive for future law and science research into robotics adoption. The model will revolutionize the methodology for constructing innovation theories. It will contribute to foundational responsible innovation research and comparative law, especially how groups interpret legal uses of robotics and how robotics impacts expectations of rights and responsibilities of agencies and developers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自2001年以来，机器人技术创新（无人地面、空中和海上系统）已被应急管理机构零星地用于灾害响应。灾难造成了一种两难境地：一方面，人们往往有一种情绪上的冲动，想尝试任何事情，希望能应对巨大的潜在生命和生计损失;另一方面，考虑不周地将机器人引入灾难可能导致比什么都不做更糟糕的结果。EARLY探索性研究概念补助金（EAGER）研究将汇集机器人，法律，应急管理和公共卫生领域的领导者，他们在应急管理和政策方面具有专业知识，以调查COVID-19大流行期间的机器人创新和伦理问题。其结果将是第一个负责任的灾难机器人创新理论。这将改变响应者在灾难期间选择机器人技术的方式，最终拯救生命，减轻长期环境和健康影响，并加速经济复苏。 该项目将为前瞻性治理提供证据，例如新的法规和政策，以加速在灾难期间采用安全有效的机器人，同时减少部署不健全技术或推迟技术部署的负面影响。该项目将影响工程，法律和政策的教学方式，培养研究生和本科生在科学和社会的多学科方法，并增加学生在研究管道的多样性。多学科团队将进行严格的分析，包括与全球各地在疫情期间部署机器人的临床医疗服务提供者、公共卫生和公共安全官员进行结构化访谈，以了解对采用的影响。需求分析将通过对用例的机器人能力进行定量分类的先前工作进行补充;这些以用户为中心和以机器人为中心的影响的正交集合将共同创建用于描述未来创新的新模板。该项目将探索法律的系统以及它们如何适应大流行的紧急情况，特别是与国家机器人政策的任何相关性，并调查紧急的伦理问题。由此产生的定量模型预计将对政策具有规范性，并对未来机器人技术采用的法律和科学研究具有预测性。该模型将使创新理论的构建方法发生革命性的变化。它将有助于基础性的负责任创新研究和比较法，特别是团体如何解释机器人技术的法律的使用，以及机器人技术如何影响机构和开发人员的权利和责任的期望。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Analysis of the Use of Robots for the Second Year of the COVID-19 Pandemic

COVID-19 大流行第二年的机器人使用情况分析

• DOI: 10.1109/ssrr56537.2022.10018671
• 发表时间: 2022
• 期刊: and Rescue Robotics (SSRR
• 作者: Murphy, Robin;Kathasagaram, Amrita;Millican, Truitt;Clendenin, Angela;deWitte, Paula;Moats, Jason
• 通讯作者: Moats, Jason

AI reflections in 2021

2021 年人工智能反思

• DOI: 10.1038/s42256-021-00435-7
• 发表时间: 2022
• 期刊: Nature Machine Intelligence
• 影响因子: 23.8
• 作者: Buckner, Cameron;Miikkulainen, Risto;Forrest, Stephanie;Milano, Silvia;Zou, James;Prunk, Carina;Irrgang, Christopher;Cohen, I. Glenn;Su, Hao;Murphy, Robin R.
• 通讯作者: Murphy, Robin R.

Adoption of Robots for Disasters: Lessons from the Response to COVID-19

• DOI: 10.1561/2300000062
• 发表时间: 2021
• 期刊: Found. Trends Robotics
• 作者: R. Murphy;Vignesh Babu Manjunath Gandudi;Justin Adams;A. Clendenin;Jason B. Moats

Robotics Responds to the COVID-19 Outbreak [From the Guest Editors]

机器人技术应对 COVID-19 爆发 [来自客座编辑]

• DOI: 10.1109/mra.2020.3048866
• 发表时间: 2021
• 期刊: IEEE Robotics & Automation Magazine
• 影响因子: 5.7
• 作者: Marques, Lino;Murphy, Robin;Althoefer, Kaspar;Tadokoro, Satoshi;Laschi, Cecilia
• 通讯作者: Laschi, Cecilia