FW-HTF-R/Collaborative Research: FAIR4WISE: Future AI and Robotics for Women in Smart Engineering

FW-HTF-R/合作研究：FAIR4WISE：智能工程领域女性的未来人工智能和机器人技术

• 批准号: 2222810
• 负责人: Shuai Li
• 金额: $ 68.86万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222810&HistoricalAwards=false
• 关键词: FW; HTF; Collaborative; Research; FAIR4WISE

This Future of Work at the Human-Technology Frontier Research (FW-HTF-R) grant will develop a new robot teleoperation method based on deep learning and blockchain certification to augment construction workers’ capability and promote diversity, equity, and inclusiveness in the workplace. By some estimates, a large fraction of construction jobs will be automated and teleoperated with robots in the future. This transition can enable safe and remote work away from hazardous construction sites with the potential to reduce obstacles for women to join the industry while also creating an inclusive work environment. At the same time, it is also important to improve the gender diversity of the construction industry, where women and other minority workers represent less than 10% of the workforce. In light of this, the project will investigate gender differences in collaborating and teleoperating robots, and capitalize on the understandings to develop robot learning and teleoperation methods that are accessible and equitable across genders. A novel blockchain-based mechanism will also be created to assess workers’ competence and performance to improve fairness and equity in future construction jobs. This research will also measure the impacts of developed technologies on future construction work, characterizing the intended potential and unintended consequences on workers and organizations. If successful, the developed technology ecosystem will help improve worker productivity, safety, and health, and equip the U.S. workers to lead the way in the construction industry reform in a gender-inclusive manner. This project can break down many barriers facing women and other underrepresented workers, opening new and equal work opportunities, helping them participate in the workforce, and navigating them in the transitions to the era of robots and artificial intelligence. This will benefit the construction industry and other domains with less diversity such as manufacturing and agriculture and result in U.S. economic growth.This project brings together an interdisciplinary team with deep and cross-cutting expertise in engineering, computer and information science, human factors, industrial and organizational psychology, education and adult training, and legal affairs to achieve multiple convergent objectives. First, this project will 1) develop an inclusive robot teleoperation interface adaptive to construction workers considering gender-related diversity and experience to augment workers’ performance; 2) design a federated learning mechanism for aggregating limited data from underrepresented workers to mitigate bias in AI and robot intelligence development; and 3) develop a blockchain-based platform in certifying workers’ skill competence and performance for trusted and equitable recruitment, hiring, and retaining. Second, with deep industry engagement, this research will develop a theoretical framework and multidimensional impact models to 1) quantitatively measure to what extent inclusive teleoperation can support gender diversity and augment workers’ capability via job and task analysis; 2) understand the impacts on construction work structure, job design, and worker self-efficacy and career development with broader participation of underrepresented workers; and 3) assess the opportunities and barriers at the organizational level for adaptations from integrated technological, economic, social, and legal aspects. Third, this project will develop a new platform integrating adult learning theories, innovative engineering curricula, and the developed artificial intelligence and robot technologies to break the boundaries for inclusive student learning, workforce training, and industry networking.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.

人类技术前沿研究的未来工作 (FW-HTF-R) 拨款将开发一种基于深度学习和区块链认证的新型机器人远程操作方法，以增强建筑工人的能力并促进工作场所的多样性、公平性和包容性据估计，未来很大一部分建筑工作将通过机器人实现自动化和远程操作，这种转变可以使安全和远程工作远离危险的建筑工地，有可能减少女性加入该行业的障碍，同时也创造了一个新的环境。包容性的工作环境。同时，改善建筑行业的性别多样性也很重要，女性和其他少数族裔工人占劳动力的比例不到10%。有鉴于此，该项目将调查协作和远程操作机器人中的性别差异。利用这些理解来开发跨性别均可使用且公平的机器人学习和远程操作方法，该研究还将创建一种基于区块链的新颖机制来评估工人的能力和绩效，以提高未来建筑工作的公平性和公平性。衡量发达技术的影响未来的建筑工作，描述对工人和组织的预期潜力和意外后果。如果成功，发达的技术生态系统将有助于提高工人的生产力、安全和健康，并使美国工人在建筑业改革中发挥引领作用。该项目可以打破女性和其他代表性不足的工人面临的许多障碍，开辟新的平等工作机会，帮助她们参与劳动力市场，并引导她们向机器人和人工智能时代过渡。有利于建设该项目汇集了一个跨学科团队，在工程、计算机和信息科学、人为因素、工业和组织心理学、教育等领域拥有深厚的跨领域专业知识首先，该项目将 1) 开发一个适应建筑工人的包容性机器人远程操作界面，考虑到性别相关的多样性和经验，以提高工人的绩效；2) 设计联合学习机制。用于聚合来自代表性不足的工人的数据有限，以减少人工智能和机器人智能开发中的偏见；3）开发一个基于区块链的平台，以认证工人的技能能力和绩效，以实现可信和公平的招聘、雇用和保留。这项研究将开发一个理论框架和多维影响模型，以 1) 通过工作和任务分析定量衡量包容性远程操作可以在多大程度上支持性别多样性并增强工人的能力；2) 了解对建筑工作结构、工作设计和工作的影响；工人代表性不足的工人更广泛地参与自我效能和职业发展；3）从综合技术、经济、社会和法律方面评估组织层面的适应机会和障碍第三，该项目将开发一个整合成人的新平台。学习理论、创新工程课程以及开发的人工智能和机器人技术，打破了包容性学生学习、劳动力培训和行业网络的界限。该奖项是 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持和广度影响审查标准。

项目成果

Underground infrastructure detection and localization using deep learning enabled radargram inversion and vision based mapping

• DOI: 10.1016/j.autcon.2023.105004
• 发表时间: 2023-10
• 期刊: Automation in Construction
• 影响因子: 10.3
• 作者: Mengjun Wang;Da Hu;Junjie Chen;Shuai Li