I-Corps: Autonomous Robotic Instructor for Workforce Training

这个i-Corps项目的更广泛的影响/商业潜力将是定义开发培训下一代劳动力的技术所需的要求，并缩小先进制造业的技能差距。制造研究所估计，到2025年，美国制造业将需要增加200万个工作岗位由劳动力填补。仅靠传统的学校和学徒制无法满足日益增长的熟练劳动力需求。中小型制造商(SMM)指出，在先进制造领域存在“技能缺口”，同时收到的订单多于他们所能填补的数量。中小企业寻求雇佣更多的工人来提高产量，但他们找不到足够的具备必要技能的工人。目前，对这些问题最成功的解决方案是为中小企业提供刚刚高中毕业的工人。然而，培训成本和培训工人所花费的时间是很大的障碍。这项拟议的技术旨在帮助公司培训现有工人，使其具备在现代制造业经济中竞争所需的技能。i-Corps项目基于自主机器人系统的开发，该系统体现了劳动力培训的互动学习。拟议的机器人技术是一名教员，从远程在线学习系统提供教育材料。这项技术也是一种演示身体动作并与学习者进行身体互动的机器。通过通过物理演示和互动来简化教学，该团队建立了一种独特的教学方法，可以让广泛的人群参与进来，包括那些没有接受过工程培训的人。为了实现这一点，已经开发了一种软件体系结构来控制机器人并促进稳健的教学。该软件是基于云和模块化设计的，因此不同的工业机器人可以很容易地交换，只需最少的重新配置。此外，这种方法代表了第一个基于用户学习方式和背景实时定制其课程的教育机器人，通过收集学习者在课程过程中的数据并做出相应的决定。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。

The broader impact/commercial potential of this I-Corps project will be to define the requirements needed to develop technology to train the next generation workforce and narrow the skills gap in advanced manufacturing. By the year 2025, the Manufacturing Institute estimates that the US manufacturing sector will demand 2 million more jobs be filled by the labor force. Traditional schools and apprenticeships alone cannot meet the increasing demand for skilled labor. Small- to medium-sized manufacturers (SMMs) cite a “skills gap” in advanced manufacturing while receiving more orders than they can fill. The SMMs seek to hire more workers to increase output, but they cannot find enough workers with the necessary skills. Currently, the most successful solution to these problems focuses on providing SMMs with workers just out of high school. However, training costs and the time spent training workers are significant barriers. This proposed technology aims to help companies train current workers with the skills necessary to compete in the modern manufacturing economy.This I-Corps project is based on the development of an autonomous robotic system that embodies interactive learning for workforce training. The proposed robotic technology is an instructor that delivers educational materials from a remote online learning system. The technology also is a machine that demonstrates physical actions and that physically interacts with learners. By streamlining instruction with physical demonstration and interaction, the team establishes a unique pedagogical approach that may engage a broad population, including those having no engineering training. To accomplish this, a software architecture has been developed to control the robot and facilitate robust teaching. The software is cloud-based and modularly designed so that different industrial robots may be easily exchanged with minimal reconfiguration. In addition, this methodology represents the first educational robot that customizes its curriculum in real-time based on user learning style and background by collecting data on the learner as they progress through the course and making decisions accordingly.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.