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.

这项未来人类技术前沿研究（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，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由机器人自动化和远程操作。这一过渡可以使人们能够在远离危险建筑工地的地方安全和远程工作，有可能减少妇女加入该行业的障碍，同时也创造了一个包容性的工作环境。与此同时，改善建筑业的性别多样性也很重要，因为妇女和其他少数民族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的性别差异，并利用这些理解来开发跨性别可访问和公平的机器人学习和远程操作方法。还将创建一个新的基于区块链的机制来评估工人的能力和表现，以提高未来建筑工作的公平性和公正性。这项研究还将衡量发达技术对未来建筑工作的影响，表征对工人和组织的预期潜在和意外后果。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，并使美国工人能够以性别包容的方式引领建筑业改革。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，帮助他们参与劳动力，并在向机器人和人工智能时代的过渡中引导他们。这将使建筑业和其他多样性较低的领域，如制造业和农业，并导致美国的经济增长。该项目汇集了一个跨学科的团队，在工程，计算机和信息科学，人的因素，工业和组织心理学，教育和成人培训，以及法律的事务，以实现多个收敛的目标。首先，该项目将1）开发一个包容性的机器人遥操作界面，适应建筑工人，考虑与性别相关的多样性和经验，以提高工人的表现; 2）设计一个联邦学习机制，用于聚合来自代表性不足的工人的有限数据，以减轻人工智能和机器人智能发展的偏见;以及3）开发一个基于区块链的平台，以认证工人的技能能力和表现，以实现可信和公平的招聘，雇用和保留。第二，在深入的行业参与下，本研究将建立一个理论框架和多维影响模型，以1）定量衡量包容性远程操作在多大程度上支持性别多样性，并通过工作和任务分析提高工人的能力; 2）了解在更广泛的参与下，代表性不足的工人对建筑工作结构，工作设计，工人自我效能和职业发展的影响;从技术、经济、社会和法律的综合方面评估组织层面的适应机会和障碍。第三，该项目将开发一个新的平台，将成人学习理论、创新工程课程以及人工智能和机器人技术的发展相结合，以打破包容性学生学习、劳动力培训和行业网络的界限。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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