ROBO-VI: A Virtual-Internship-Based Hybrid Learning Technology to Prepare Traditional and Non-Traditional Students to Work with Collaborative Robots

ROBO-VI：一种基于虚拟实习的混合学习技术，帮助传统和非传统学生做好使用协作机器人的准备

• 批准号: 1822872
• 负责人: Bilge Mutlu
• 金额: $ 49.91万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822872&HistoricalAwards=false
• 关键词: ROBO; VI; Virtual; Internship; Based

Collaborative robots are emerging as a new family of advanced technologies that are designed to work side-by-side with people in industrial settings. These robots can improve the productivity and flexibility in manufacturing and logistics industries, while also assisting workers in repetitive, and unhealthy or unsafe work conditions. Unlike traditional factory robots that are programmed and configured by engineers to function independently for long periods of time, collaborative robots require human workers to frequently interact with the robot, including training the robot, making the necessary changes in the environment for the robot to function, and supervising the robot's work to ensure its successful operation. The integration of collaborative robots into and operation within existing industrial settings require expert skills that many workers lack, and that current educational programs do not cover, highlighting a skills mismatch from the demands of an increasingly automated future of work. This project will develop an understanding of what skills workers will need to perform these tasks effectively and to design a hybrid digital-physical educational technology that will support worker education and training in these skills in classrooms and industrial-training facilities. The research team will evaluate the effects of the technology on skill development among trainees and make recommendations for future job-training programs. The new hybrid technology will include an expert system computer program that will provide learners with explanations, visualizations, and simulations of key concepts and a robotic assistant that will provide physical demonstrations. This hybrid physical-digital learning environment will be used in expert-led instruction of traditional and non-traditional students in collaborative robotics. The research team will (1) develop an empirical model of expertise in working with collaborative robots through observations of and interviews with experts and analyses of expert-training programs and (2) iteratively design, build, and test a digital-physical hybrid learning environment, integrating an Expert-View Dashboard (EVD), to support expert-led instruction in educational and industrial-training settings. The resulting educational technology will provide educators and trainers with a powerful educational tool that will supplement the development of expertise in working with autonomous, intelligent, and collaborative technologies to meet the demands of an increasingly automated jobs landscape.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.

协作机器人正在崛起，成为一系列先进的新技术，旨在与工业环境中的人们并肩工作。这些机器人可以提高制造业和物流业的生产率和灵活性，同时还可以帮助工人在重复、不健康或不安全的工作条件下工作。与由工程师编程和配置以长时间独立工作的传统工厂机器人不同，协作式机器人需要人类工人经常与机器人互动，包括训练机器人，对机器人运行所需的环境进行必要的改变，以及监督机器人的工作以确保其成功运行。将协作机器人整合到现有工业环境中并在现有工业环境中操作需要许多工人缺乏的专业技能，而当前的教育计划没有涵盖这些技能，这突显出与日益自动化的未来工作的需求不匹配的技能。该项目将加深对工人需要哪些技能才能有效执行这些任务的理解，并设计一种数字-体育混合技术，支持在教室和工业培训设施中对工人进行这些技能的教育和培训。研究小组将评估这项技术对学员技能发展的影响，并为未来的就业培训计划提出建议。新的混合技术将包括一个专家系统计算机程序，它将为学习者提供关键概念的解释、可视化和模拟，以及一个将提供物理演示的机器人助手。这种物理-数字混合学习环境将用于在协作机器人学中对传统和非传统学生进行专家引导的教学。研究团队将(1)通过对专家的观察和采访以及对专家培训计划的分析，开发与协作机器人合作的专业知识经验模型，以及(2)反复设计、构建和测试数字-物理混合学习环境，集成专家-视图仪表板(EVD)，以支持教育和工业培训环境中的专家指导教学。由此产生的教育技术将为教育工作者和培训人员提供强大的教育工具，补充自主、智能和协作技术的专业知识开发，以满足日益自动化的就业环境的需求。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。

项目成果

Designing Interface Aids to Assist Collaborative Robot Operators in Attention Management

设计界面辅助工具以协助协作机器人操作员进行注意力管理

• 发表时间: 2021
• 期刊: IEEE International Conference on Robot and Human Interactive Communication
• 作者: Henrichs, Curt;Zhao, Fangyun;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

Making Informed Decisions: Supporting Cobot Integration Considering Business and Worker Preferences

• DOI: 10.1145/3610977.3634937
• 发表时间: 2024-01
• 期刊: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Dakota Sullivan;N. White;Andrew Schoen;Bilge Mutlu

CoFrame: A System for Training Novice Cobot Programmers

CoFrame：用于培训新手协作机器人程序员的系统

• DOI: 10.5555/3523760.3523788
• 发表时间: 2022
• 期刊: HRI '22: Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Schoen, Andrew;White, Nathan;Henrichs, Curt;Siebert-Evenstone, Amanda;Shaffer, David;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

Safety first: Developing a model of expertise in collaborative robotics

安全第一：开发协作机器人专业知识模型

• DOI: 10.1007/978-3-030-67788-6_21
• 发表时间: 2021
• 期刊: Proceedings
• 作者: Siebert-Evenstone, Amanda;Michaelis, Joseph E.;Shaffer, David W.;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

Task Interdependence in Human-Robot Teaming

人机协作中的任务相互依赖

• 发表时间: 2020
• 期刊: IEEE International Conference on Robot and Human Interactive Communication
• 作者: Fangyun Zhao, Curt Henrichs