FW-HTF-RM: Human-Robot Collaboration for Manual Work

人类技术前沿（FW-HTF）的“未来工作”项目推进了机器人与工人一起工作的愿景，以增加人类活动，创造更好的工作条件，提供更高的工作满意度，开辟新的就业前景，并带来比手工劳动或自动化更高的生产力。现有的工作设计和自动化的原理和方法往往使用机器人来取代人类工人，其经济和社会效益本身是有限的。这项研究提出了一个机器人作为物理助手的模型，与人类工人合作，重新定义人类的工作。其目标是创建一个“配对”过程，以确定能够显著提高生产率、健康和安全的人机配对，同时最大限度地利用人力资源。一种多学科的方法被应用于制造业中现有的手工任务，这些任务显示出从协作机器人的集成中获益的巨大潜力，以便更好地理解人类和智能机器人助手在个人、操作和社会经济层面的影响。最后，通过调查当前的工作特征、工人技能和机器人能力获得的见解，被应用于提高下一代制造机器人对经济实力和劳动力生活质量的好处。这项研究的目的是增加对如何使用协作机器人进行体力或认知工作来增强和增强人类能力的理解；一个新的计算框架，以最佳地匹配工作特征和技能、人类能力和机器人增强；通过人机协作实现跨行业人力就业转型的创新经济模式和政策建议；以及如何设计下一代机器人系统的见解。将研究现有的制造业工作和任务，这些工作和任务显示出从协作机器人的集成中获益的巨大潜力，以了解人机配对如何显着提高经济生产力，以及工人的健康和安全。人机团队的技术挑战是在工人福利和生产的更大背景下考虑的，以及劳动力和机器人之间的可替代性，劳动力技能需求的变化，以及特定过程中技术变化时劳动力需求和供给的反应。该项目包括实验室模拟和专家评审，以验证优化的人机工作计划，并就如何设计下一代机器人系统提出建议，以进一步提高人类的工作能力和生产力。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。

This Future of Work at the Human-Technology Frontier (FW-HTF) project advances the vision of robots that work alongside workers to augment human activities, create better working conditions, provide more job satisfaction, open new employment prospects, and result in greater productivity than manual labor or automation alone. Existing principles and methods for work design and automation often use robots to displace human workers and are inherently limited in their economic and social benefits. This research advances a model of robots as physical assistants, working in partnership with human workers to redefine human work. The objective is to create a "matchmaking" process to identify human-robot pairings that can significantly improve productivity, health, and safety, while maximizing human labor resources. A multidisciplinary approach is applied to existing manual tasks in the manufacturing sector that show high potential to benefit from the integration of collaborative robots, in order to gain a better understanding of the impact of teaming humans and intelligent robotic assistants at the individual, operations, and socioeconomic levels. Finally, the insight gained from investigating current job characteristics, worker skills, and robot capabilities, is applied to enhance the benefits of the next generation of manufacturing robots for the strength of the economy and the quality of life of the workforce. This research aims to provide an increased understanding of how human capabilities can be augmented and enhanced using collaborative robots for physical or cognitive work; a new computational framework to optimally match work characteristics and skills, human capabilities, and robot augmentations; innovative economic models of, and policy recommendations for, the transformation of human employment across industries through human-robot collaboration; and insight into how the next generation of robot systems should be designed. Existing manufacturing sector jobs and tasks that show high potential to benefit from the integration of collaborative robots will be studied to understand how human-robot pairings can significantly improve economic productivity, and worker health and safety. The technical challenges in human-robot teaming are considered in the larger context of worker wellbeing and production as well as the substitutability between labor and robotics, changes in demand for labor skills, and the response in labor demand and supply when technology changes for a specific process. The project includes laboratory simulations and expert review to validate the optimized human-robot work plans, and recommendations on how the next generation of robot systems should be designed to further augment human capabilities and productivity in work.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.