CHS: Medium: Collaborative Research: Augmenting Human Cognition with Collaborative Robots

CHS：媒介：协作研究：用协作机器人增强人类认知

• 批准号: 2343187
• 负责人: Ranjana Mehta
• 金额: $ 41.59万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2343187&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Augmenting

Collaborative robotics is a growing application space in robot technology used in manufacturing, mining, construction, and energy industrial settings. A recent report by the International Federation of Robots indicates that global robotics spending will reach $13 billion in 2025. The largest consumers of industrial robotics have been in the Asian market, i.e. China, Japan, Republic of Korea, with the U.S. lagging behind both Europe (Germany, France, Spain) and Asia. It is in the national economic and stratgic interest to ensure that US industry and workers regain leadership in collaborative manufacturing robots. Towards that goal, this project will develop understanding of technical and socio-technical requirements to accelerate the use of collaborative robotics. The project will contribute new knowledge and theory of Human-Computer Interaction and Human-Robot Interaction, by augmenting human cognition for safer and more efficient collaborative robot interaction. The new design principles for collaborative robotic technologies will improve both the worker and the employer's growth and progress. Fundamental knowledge gained here will be directly applicable in other high-risk domains that use collaborative robots, such as offshore oil rigs, military, and construction. The project seeks to empower new populations of workers (e.g., workers with disabilities), allow older workers to remain in the workforce, and potentially assist novice workers, thereby reducing skills gaps and improving work efficiency. The team will focus on broadening the participation of females in computing. In addition to traditional academic venues (e.g. conference, journals, etc.), research results will be further disseminated through workplace workshops and seminars through existing state, regional, and national networks of employers and industry partners. To meet these goals, the team of researchers plan to: (1) develop a novel HRI task/scenario classification scheme in collaborative robotics environments that are vulnerable to system failures; (2) establish fundamental neurophysiological, cognitive, and socio-behavioral models (workload, cognitive load, fatigue/stress, affect, and trust) to monitor and model the mind motor machine nexus; (3) use these models to determine when and how a human's cognitive, social, behavioral and environmental states require adjustment via technology to enhance HRI for efficient and safe work performance; and finally (4) create an innovative and transformative Work 4.0 architecture (AMELIA: AugMEnted Learning InnovAtion) that includes a layer of augmented reality for human and robots to mutually learn and communicate current states. The team will characterize worker cognitive states inferred from their physiological data and eye tracking. They will then use embedded sensor readings, error codes, and surveillance cameras to characterize robot states. Through augmented reality, AMELIA will provide this data to both the worker and the robot for effective real-time adjustment in behaviors to mitigate failure sources and errors while ensuring minimal additional cognitive load. The team plans a novel communication scheme using artificial emotional intelligence in which robots and humans collaborate in potentially dangerous situations. The robot will detect the worker's cognitive state using different machine learning techniques, and then take the appropriate action. Ultimately AMELIA seeks to empower the worker to focus on complex, cognitive problem-solving tasks, performed safely and efficiently, while ensuring that it adapts to both the worker's attitudes and cognitive states.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.

协作机器人技术是在制造，采矿，建筑和能源工业环境中使用的机器人技术中不断增长的应用空间。国际机器人联合会最近的一份报告表明，全球机器人支出将在2025年达到130亿美元。工业机器人技术的最大消费者是亚洲市场，即中国，日本，韩国，美国落后于欧洲（德国，法国，西班牙）和亚洲。确保美国工业和工人重新获得协作制造机器人的领导才能符合国家经济和层层的利益。为了实现这一目标，该项目将发展对技术和社会技术要求的理解，以加速协作机器人技术的使用。 该项目将通过增强人类认知以进行更安全，更有效的协作机器人相互作用来贡献人类互动和人类机器人互动的新知识和理论。 合作机器人技术的新设计原则将改善工人和雇主的成长和进步。这里获得的基本知识将直接适用于使用合作机器人（例如海上石油钻机，军事和建筑）的其他高风险领域。该项目旨在赋予新的工人人群（例如，残疾工人），使年长的工人能够留在劳动力中，并有可能协助新手工人，从而降低技能差距并提高工作效率。 该团队将专注于扩大女性参与计算的参与。 除传统的学术场所（例如会议，期刊等）外，研究结果还将通过工作场所的研讨会和研讨会通过现有的州，地区和国家雇主和行业合作伙伴的国家网络进一步传播。为了实现这些目标，研究人员团队计划：（1）在容易受到系统故障影响的协作机器人环境中开发一种新颖的HRI任务/场景分类方案； （2）建立基本的神经生理学，认知和社会行为模型（工作量，认知负荷，疲劳/压力，情感和信任）来监视和建模思维机器Nexus； （3）使用这些模型来确定人类的认知，社会，行为和环境状态如何通过技术进行调整，以增强HRI的高效和安全工作表现；最后（4）创建了创新和变革性的工作4.0体系结构（Amelia：增强学习创新），其中包括人类和机器人相互学习和传达当前状态的增强现实层。 该团队将根据其生理数据和眼动追踪推断出的工人认知状态。然后，他们将使用嵌入式传感器读数，错误代码和监视摄像机来表征机器人状态。通过增强现实，Amelia将向工人和机器人提供此数据，以进行有效的行为实时调整，以减轻故障源和错误，同时确保最小的额外认知负担。团队计划采用人工情商的新型沟通计划，在潜在的危险情况下，机器人和人类合作。机器人将使用不同的机器学习技术检测工人的认知状态，然后采取适当的动作。 最终，阿米莉亚（Amelia）试图使工人能够专注于复杂的，认知问题解决的任务，安全，有效地执行，同时确保其适应工人的态度和认知状态。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和更广泛的crietia crietia crietia criperia criperia criperia criperia criperia criperia crigitia criperia criperia criperia rectiria均值得通过评估。