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

这一人类技术前沿的未来工作(FW-HTF)项目推进了机器人的愿景，这些机器人与工人一起工作，以增加人类活动，创造更好的工作条件，提供更多的工作满意度，开辟新的就业前景，并产生比单独的体力劳动或自动化更高的生产率。现有的工作设计和自动化的原则和方法往往使用机器人来取代人类工人，其经济效益和社会效益固有地受到限制。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。我们的目标是创建一个“配对”过程，以确定人与机器人的配对，从而显著提高生产率、健康和安全，同时最大化人力资源。将多学科方法应用于制造业中显示出从协作机器人集成中受益的高潜力的现有人工任务，以便更好地了解将人类和智能机器人助手组合在一起在个人、运营和社会经济层面上的影响。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洞察力被应用于增强下一代制造机器人对经济实力和劳动力生活质量的好处。这项研究旨在加深对如何使用协作机器人进行体力或认知工作的增强和增强人类能力的理解；一个新的计算框架，以最佳匹配工作特征和技能、人类能力和机器人增强；通过人-机器人协作实现人类就业跨行业转变的创新经济模型和政策建议；以及对应如何设计下一代机器人系统的洞察。将研究现有制造业工作和任务，这些工作和任务显示出从协作机器人的集成中受益的潜力很大，以了解人-机器人配对如何显著提高经济生产率以及工人的健康和安全。人-机器人协作中的技术挑战被放在工人福祉和生产的更大背景下考虑，以及劳动力和机器人之间的替代性，对劳动技能的需求变化，以及当特定过程的技术变化时劳动力需求和供应的响应。该项目包括实验室模拟和专家评审，以验证优化的人类-机器人工作计划，以及关于如何设计下一代机器人系统以进一步增强人类能力和工作效率的建议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.