FW-HTF-RM: Collaborative Research: Supervise It! Optimizing Intelligent Robot Integration Through Feedback to Workers and Supervisors

可实现性能和可编程性方面的限制是实现需要大量小批量任务的制造操作的完全自动化所带来的生产力提高的障碍。在不取代人类工作的情况下，使用协作机器人来协助人类工人是克服这些障碍的一种很有前途的方法。目前在这一领域的努力主要集中在工人与机器人的合作关系上，而忽视了主管在管理工作量和分配任务方面的关键作用。通过考虑监督工作团队的更大背景，这项人类技术前沿工作的未来（FW-HTF）研究旨在通过提高工人与机器人合作的效率来提高生产力和改善工人的生活质量。它提供了一个框架，用于分析工业环境中协作机器人的准备情况、评估采用情况和评估性能。与美国东南部公司的合作和互动将促进现实的研究成果转化为实践，使美国的中小型制造公司受益。为了吸引下一代劳动者和研究人员进入理工科领域，将向大众宣传这些努力和成果。这个项目探讨了两个假设。第一个可行的假设是，当工人将机器人视为合作伙伴时，如果工人能够成功地管理机器人以比他们自己设想的速度更快的速度完成任务，那么不完美是可以容忍的。关于一个工人-机器人协作伙伴关系的价值的决定因素假设是工人的能力分配任务工作量之间的机器人和自己的最优伙伴关系。第二个工作假设是，引入主管来指导和促进任务分配将进一步有助于提高工人-机器人的绩效。这一假设建立在一线主管与多名工人互动的观察基础上，因此是关于良好实践的整体制度知识的储存库。为了证实这些假设并得出预期的框架，即工人-机器人主管效率模型，需要混合方法的研究设计。首先，有根据的理论研究将建立与工人、机器人和主管技术采用和绩效相关的评估标准，以开发一种测量两者的工具。其次，在模拟的制造工作单元环境中，一系列实验将调查与成功的技术采用和工作单元性能相关的因素。第三，这些研究的结果将为模型的创建提供信息，这将为有效整合、采用和监督工人与机器人的伙伴关系提供指导。第四，为了确认衍生模型的适用性，它将被用于在现实世界公司的现有流程中部署和集成机器人。现场部署将为验证和完善模型提供经验证据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Limitations in achievable performance and programmability are obstacles to realizing productivity gains from the full automation of manufacturing operations requiring a large variety of low-volume tasks. The use of collaborative robots to assist human workers is a promising approach to overcoming these obstacles, without displacing human jobs. Current efforts in this area focus on the worker-robot partnership and overlook the critical role of the supervisor in managing workloads and allocating tasks. By considering the larger context of supervised work teams, this Future of Work at the Human-Technology Frontier (FW-HTF) research aims to enhance productivity and improve worker quality of life by increasing the effectiveness of workers operating in partnership with robots. It provides a framework for analyzing readiness, assessing adoption, and evaluating performance of collaborative robotics in industrial settings. Partnerships and interactions with companies in the Southeastern USA will promote realistic research efforts that translate to practice, benefitting small-to-medium manufacturing companies in the USA. Efforts and findings will be promoted to the public to attract the next generation of workers and researchers to science and engineering fields.This project explores two hypotheses. The first working hypothesis is that, when workers view robots as partners, imperfection will be tolerated if the worker can successfully manage the robot to complete the task faster than their self-conceived rate. The determining factor regarding the value of a worker-robot collaborative partnership is hypothesized to be the worker’s ability to allocate the task workload between the robot and themselves towards an optimal partnership. The second working hypothesis is that the introduction of a supervisor to guide and promote task allocation will further contribute to enhanced worker-robot performance. This hypothesis builds on the observation that line supervisors interact with multiple workers, and thus are a repository of holistic institutional knowledge regarding good practice. To confirm these hypotheses and arrive at the anticipated framework, called the Worker-Robot Supervisor Effectiveness Model, requires a mixed-methods research design. First, a grounded theory study will establish assessment criteria related to worker, robot, and supervisor technology adoption and performance to develop an instrument for measuring both. Second, within a simulated manufacturing work cell environment, a set of experiments will investigate factors linked to successful technology adoption and work cell performance. Third, the findings from these studies will inform the creation of the model, which will provide guidance for effective integration, adoption, and supervision of worker-robot partnerships. Fourth, to confirm the applicability of the derived model, it will be used to field and integrate a robot within the existing processes of a real-world company. Field deployment will provide empirical evidence to validate and refine the model.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.