I-Corps: Autonomous Robotic Instructor for Workforce Training

这个I-Corps项目的更广泛的影响/商业潜力将是确定开发技术所需的要求，以培训下一代劳动力，缩小先进制造业的技能差距。美国制造业协会估计，到2025年，美国制造业将需要200多万个就业岗位。传统的学校和学徒制不能满足对熟练劳动力日益增长的需求。中小型制造商（smm）表示，在接受订单多到无法完成的情况下，先进制造业存在“技能缺口”。中小企业试图雇佣更多的工人来增加产量，但他们找不到足够多拥有必要技能的工人。目前，解决这些问题最成功的办法是为中小企业提供刚从高中毕业的工人。然而，培训成本和培训工人所花费的时间是重大障碍。这项拟议中的技术旨在帮助企业培训现有工人，使其具备在现代制造业经济中竞争所必需的技能。这个I-Corps项目是基于自主机器人系统的开发，该系统体现了用于劳动力培训的交互式学习。提出的机器人技术是一个讲师，从远程在线学习系统提供教育材料。这项技术也是一种机器，可以展示物理动作，并与学习者进行物理互动。通过物理演示和互动的精简教学，团队建立了一种独特的教学方法，可以吸引广泛的人群，包括那些没有受过工程培训的人。为了实现这一目标，开发了一个软件架构来控制机器人并促进稳健的教学。该软件是基于云计算和模块化设计的，因此不同的工业机器人可以很容易地以最小的重新配置进行交换。此外，这种方法代表了第一个教育机器人，它可以根据用户的学习风格和背景实时定制课程，在学习者学习过程中收集他们的数据并做出相应的决定。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。

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.