NRI: FND: End-User Robot Programming

协作机器人，或者说可以安全地在人类附近工作的机器人，有望彻底改变社会。 这种机器人可以帮助该国在服务业和制造业保持竞争力。 但这一愿景尚未实现，因为对机器人编程仍然困难、耗时，而且最重要的是，只有经过多年培训的专家才可行。 该项目旨在通过使协作机器人可由其所有者编程来消除这一最后的障碍。 也就是说，在没有任何特殊培训的情况下，让小企业主、实验室技术人员和小规模制造商能够对机器人进行编程，完成有意义的任务。 项目成果不仅将通过降低成本和解放人力来完成更高级别的任务，使美国企业受益，还将改善许多人获得技术的机会。 由于这项研究将在一所高度多样化的大学进行，并与一所农村社区学院和一个农村制造业研究中心合作，因此将通过让不同的学生群体接触他们从未知道过的尖端技术来产生额外的广泛影响。 此外，通过在两个农村地区进行实验和演示，该团队将向更广泛的社区传授未来职业选择的愿景，同时将机器人编程技术推向新的水平。目前，编程协作机器人的高成本仍然是采用的主要障碍。 该项目将创建一套最终用户编程语言，使新手可以访问常见的机器人编程任务，大大减少了采用的障碍。 这项工作是基于两个关键的见解。 首先，由于协作机器人提供了前所未有的安全功能，最终用户可以安全地操作和编程。 其次，通过利用过去二十年来教育编程语言的进步（例如，麻省理工学院的Scratch），可以创建支持现实任务编程的接口，但可以在几分钟内学会。 该项目将包括四个重点：（1）创建一个基于块的机器人编程环境，使大量的现实任务;（2）开发一个在线教程系统，用于学习基于块的编程，而无需正式培训;（3）实现多臂编程的并行编程语言;以及（4）定义多线程编程方法，以最大限度地提高机器人的使用率。 这套全面的工具将使最终用户能够解决目前无法实现的许多常见机器人编程任务。 为确保各种方法切实帮助最终用户，将通过在城市和农村大学校园进行广泛的用户研究对这些方法进行评估。 这些研究中，受试者用原型完成现实任务，将有三个结果。 首先，为研究做准备自然会将研究原型强化为高质量的软件，从而允许准确的评估。 第二，这项研究将产生关于使用每种工具编制最终用户方案的实效的可量化结果。 第三，通过视频分析和访谈，将进一步深入了解阻碍最终用户的问题，从而为下一轮创新奠定基础。该奖项反映了NSF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。

Collaborative robots, or robots that can safely work in close proximity to humans, promise to revolutionize society. Such robots could help the country remain competitive in the service and manufacturing industries. But this vision has not yet been achieved because programming robots remains difficult, time consuming, and, most importantly, only feasible by experts after years of training. This project seeks to remove this final barrier to progress by making collaborative robots programmable by their owners. That is, without any special training, to empower small business owners, lab technicians, and small-scale manufacturers to be able to program a robot to do meaningful tasks. Project outcomes will not only benefit American businesses by lowering costs and freeing human workers to complete higher-level tasks, they will also improve access to technology for many. Because the research will be conducted at a highly diverse university, in collaboration with a rural community college and a rural manufacturing research center, additional broad impacts will derive from exposing a diverse group of students to cutting edge technology they would otherwise never know existed. Furthermore, by conducting experiments and demonstrations at both rural sites the team will impart to the broader community a vision for future career choices while driving robot programming technology to the next level.Currently, the high cost of programming collaborative robots remains a major barrier to adoption. This project will create a suite of end-user programming languages that make common robot programming tasks accessible to novices, dramatically reducing this barrier to adoption. The work is based on two key insights. First, because collaborative robots offer unprecedented safety features, end-users can safely operate and program them. Second, by leveraging the last twenty years of progress in educational programming languages (e.g., MIT’s Scratch), interfaces can be created that support the programming of realistic tasks yet can be learned in minutes. The project will comprise four thrusts: (1) creation of a block-based robot programming environment that enables a large set of realistic tasks; (2) development of an in-line tutorial system for learning block-based programming without formal training; (3) implementation of a parallel programming language for multi-armed programming; and (4) definition of a multi-threaded programming approach for maximizing robot usage. This comprehensive set of tools will enable end-users to tackle many common robot programming tasks that are currently out of their reach. To ensure that the various approaches actually help end-users, they will be evaluated through extensive user studies run both on an urban and rural university campus. These studies, where subjects complete realistic tasks with the prototypes, will have three outcomes. First, preparing for the studies naturally hardens the research prototypes into high-quality software, allowing accurate evaluation. Second, the study will generate quantifiable results on the effectiveness of end-users programming with each tool. Third, through video analysis and interviews additional insight will be derived into further issues that hinder end-users, thereby laying the groundwork for the next round of innovations.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.