Integrating Robots into the Future of Work
将机器人融入未来的工作
基本信息
- 批准号:2152163
- 负责人:
- 金额:$ 300万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-07-01 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Recent technical advances have enabled a new family of robotic technologies, termed collaborative robots, or “cobots.” Unlike traditional “caged” industrial robots that are limited to isolated factory environments, cobots make it safe and practical for robots to work in the same environments in which people work and live. These technologies have the potential to fundamentally transform how physical work is performed in industries from manufacturing to services and in occupations ranging from manual labor to highly trained professions. Rather than automating away human labor, cobots have the potential to enhance productivity, safety, and ergonomic conditions for human workers. Realizing this potential will require not only advancing technology to create sufficiently capable robots but also addressing human-centered questions ranging from how individuals interact with robots to how the introduction of robots affects personal life, the workplace, job satisfaction, communities and societies. Despite these broad-ranging and interdisciplinary research challenges, robotic technology is primarily driven by scientists with deep technical backgrounds but little experience with human-centered and societal issues. On the other hand, social scientists studying how robots might affect human work may lack an understanding of the capabilities of robotic technology as well as the possibilities enabled by them. Addressing these challenges will require a new generation of “STEM+” researchers capable of marrying technical and social-scientific knowledge and skills to carry out rigorous, groundbreaking fundamental research at the boundary of people and technology. The INTEGRATE training program provides a unique academic environment to train graduate students in STEM disciplines, including computer science, engineering, psychology, and economics, to become technologists and social scientists equipped to address the significant technical and human-centric challenges at the individual, organizational, and societal scales. The program achieves this goal through key innovations in STEM curricula, experiential research training, professional development opportunities, community development, and program evaluation. Trainees who complete the program will help form a new generation of interdisciplinary researchers who will work across disciplinary boundaries to shape robotic technologies and the future of work in a way that maximizes individual and societal benefit. In addition to advancing and enriching STEM training, the program makes significant societal contributions through new technologies that will improve the lives of workers; new knowledge and guidelines that will improve organizations and industries; and new recommendations that inform labor practices, technology policy, and the legal system.The INTEGRATE program provides a unique academic environment to enrich the training of graduate students in STEM disciplines, including computer science, engineering, psychology, and economics, to address fundamental research challenges in realizing the integration of robots into the future of work. The program achieves these goals through five key components and innovations. The first is a flexible, personalized curriculum that combines core STEM training with coursework from complementary human-centered disciplines in the form of a set of concentration areas. The second component consists of hands-on, immersive, and mentored research experiences through apprenticeship-based industry-sponsored team research projects on real-world problems, called “Expeditions.” The third element is comprised of opportunities for professional development through internships and secondments at a network of partner industrial and academic organizations. Fourth is the forming of an INTEGRATE Research Community through weekly colloquia, an annual “INTEGRATE Week” event, and significant online presence and activity. Fifth and finally is continuous and rigorous assessment of program effectiveness in training, research productivity, and real-world relevance and impact toward program refinement. This highly interdisciplinary program brings together faculty from computer sciences; mechanical and industrial & systems engineering; psychology; educational psychology; business; law; and public affairs/economics. Throughout the program, trainees participate in research that investigates research challenges at the micro, systems, and macro scales. At the micro level, research includes the design of novel interfaces and algorithms to enable effective teaming between people and robots; studies of how humans build working relationships with robots; investigations of the impact of working with robots on human health, safety, life and job satisfaction, and wellbeing; and development of training programs for people to effectively work with robots. At the systems level, trainees study the integration of robotic assistants into home life, the workplace, and industrial processes; how they must be redesigned to best utilize cobots; how robots change organizations; and the changes in organizational behavior and adoption around robots. At the macro scale, trainee research teams will investigate how the introduction of robots affects human labor across demographic groups and communities and develops public policy changes that might be necessary to maximize the societal utility of robotic technologies. In the program, trainees build research skills that integrate multiple disciplinary perspectives, knowledge, and skills as well as core professional skills in communication, teamwork, and ethical decision making.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.
最近的技术进步催生了一系列新的机器人技术,称为协作机器人或“cobots”。与仅限于孤立的工厂环境的传统“笼式”工业机器人不同,协作机器人使机器人能够在与人们工作和生活相同的环境中安全实用地工作。这些技术有可能从根本上改变从制造业到服务业以及从体力劳动到训练有素的职业等各种职业中体力劳动的执行方式。协作机器人并没有使人类劳动自动化,而是有潜力提高人类工人的生产力、安全性和人体工学条件。实现这一潜力不仅需要先进的技术来创造足够强大的机器人,还需要解决以人为本的问题,从个人如何与机器人互动到机器人的引入如何影响个人生活、工作场所、工作满意度、社区和社会。尽管存在这些广泛和跨学科的研究挑战,机器人技术主要是由具有深厚技术背景但对以人为中心的社会问题缺乏经验的科学家推动的。另一方面,研究机器人如何影响人类工作的社会科学家可能缺乏对机器人技术的能力及其所带来的可能性的了解。应对这些挑战需要新一代“STEM+”研究人员能够将技术和社会科学知识和技能结合起来,在人与技术的边界上开展严格的、开创性的基础研究。 INTEGRATE 培训计划提供了一个独特的学术环境,培训 STEM 学科(包括计算机科学、工程学、心理学和经济学)的研究生成为技术专家和社会科学家,有能力应对个人、组织和社会层面的重大技术和以人为本的挑战。该项目通过 STEM 课程、体验式研究培训、专业发展机会、社区发展和项目评估方面的关键创新来实现这一目标。完成该计划的学员将帮助形成新一代跨学科研究人员,他们将跨学科边界开展工作,以最大限度地提高个人和社会利益的方式塑造机器人技术和工作的未来。除了推进和丰富 STEM 培训之外,该计划还通过改善工人生活的新技术做出了重大社会贡献;将改善组织和行业的新知识和指导方针; INTEGRATE项目提供了一个独特的学术环境,以丰富STEM学科(包括计算机科学、工程学、心理学和经济学)研究生的培训,以解决实现机器人融入未来工作的基础研究挑战。该计划通过五个关键组成部分和创新来实现这些目标。第一个是灵活、个性化的课程,它将核心 STEM 培训与以人为本的互补学科的课程结合起来,以一组集中领域的形式进行。第二个部分包括通过基于学徒制的行业赞助的针对现实问题的团队研究项目(称为“探险”)的实践、身临其境和指导的研究经验。第三个要素包括通过在工业和学术组织合作伙伴网络实习和借调来实现专业发展的机会。第四是通过每周座谈会、年度“整合周”活动以及重要的在线展示和活动来形成整合研究社区。第五也是最后一点是对培训、研究生产力以及现实世界的相关性和对项目细化的影响方面的项目有效性进行持续和严格的评估。这个高度跨学科的项目汇集了计算机科学领域的教师;机械和工业及系统工程;心理学;教育心理学;商业;法律;和公共事务/经济。在整个项目中,学员参与研究微观、系统和宏观层面的研究挑战。在微观层面,研究包括设计新颖的界面和算法,以实现人与机器人之间的有效协作;研究人类如何与机器人建立工作关系;调查使用机器人工作对人类健康、安全、生活和工作满意度以及福祉的影响;制定培训计划,使人们能够有效地使用机器人。在系统层面,学员学习如何将机器人助手集成到家庭生活、工作场所和工业流程中;如何重新设计它们才能最好地利用协作机器人;机器人如何改变组织;以及围绕机器人的组织行为和采用的变化。在宏观层面上,受训研究团队将研究机器人的引入如何影响不同人口群体和社区的人类劳动力,并制定公共政策变化,以最大限度地发挥机器人技术的社会效用。在该计划中,学员将培养整合多学科观点、知识和技能的研究技能,以及沟通、团队合作和道德决策方面的核心专业技能。 NSF 研究培训计划 (NRT) 旨在鼓励为 STEM 研究生教育培训开发和实施大胆的、具有潜在变革性的新模式。该计划致力于通过创新、循证并符合不断变化的劳动力和研究需求的综合培训模式,对高度优先的跨学科或融合研究领域的 STEM 研究生进行有效培训。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力价值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
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Making Informed Decisions: Supporting Cobot Integration Considering Business and Worker Preferences
- DOI:10.1145/3610977.3634937
- 发表时间:2024-01
- 期刊:
- 影响因子:0
- 作者:Dakota Sullivan;N. White;Andrew Schoen;Bilge Mutlu
- 通讯作者:Dakota Sullivan;N. White;Andrew Schoen;Bilge Mutlu
Handheld Haptic Device with Coupled Bidirectional Input
具有耦合双向输入的手持式触觉设备
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Doshi, M. V.;Hagenow, M.;Radwin, R.;Gleicher, M.;Mutlu, B.;Zinn, M.
- 通讯作者:Zinn, M.
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Bilge Mutlu其他文献
Robust, low-cost, non-intrusive sensing and recognition of seated postures
稳健、低成本、非侵入式的坐姿传感和识别
- DOI:
10.1145/1294211.1294237 - 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
Bilge Mutlu;Andreas Krause;J. Forlizzi;Carlos Guestrin;J. Hodgins - 通讯作者:
J. Hodgins
The Social Impact of a Robot Co-Worker in Industrial Settings
工业环境中机器人同事的社会影响
- DOI:
10.1145/2702123.2702181 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Allison Sauppé;Bilge Mutlu - 通讯作者:
Bilge Mutlu
Characterizing Barriers and Technology Needs in the Kitchen for Blind and Low Vision People
描述盲人和低视力人士厨房中的障碍和技术需求
- DOI:
10.48550/arxiv.2310.05396 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Ru Wang;Nihan Zhou;Tam Nguyen;Sanbrita Mondal;Bilge Mutlu;Yuhang Zhao - 通讯作者:
Yuhang Zhao
Manually Acquiring Targets From Multiple Viewpoints Using Video Feedback
使用视频反馈从多个视角手动获取目标
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Bailey Ramesh;Anna Konstant;Pragathi Pravenna;Emmanuel Senft;Michael Gleicher;Bilge Mutlu;M. Zinn;R. Radwin - 通讯作者:
R. Radwin
Proceedings of the Third international conference on Social Robotics
第三届社会机器人国际会议论文集
- DOI:
- 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Bilge Mutlu;C. Bartneck;Jaap Ham;V. Evers - 通讯作者:
V. Evers
Bilge Mutlu的其他文献
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{{ truncateString('Bilge Mutlu', 18)}}的其他基金
Collaborative Research: HCC: Medium: Designing Social Companion Robots for Long-term Interaction
合作研究:HCC:媒介:设计用于长期交互的社交伴侣机器人
- 批准号:
2312354 - 财政年份:2023
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
Collaborative Research: HCC: Small: PATHWiSE - Supporting Teacher Authoring of Robot-Assisted Homework
合作研究:HCC:小型:PATHWiSE - 支持教师编写机器人辅助作业
- 批准号:
2202803 - 财政年份:2022
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
Designing and Testing Companion Robots to Support Informal, In-home STEM Learning
设计和测试伴侣机器人以支持非正式的家庭 STEM 学习
- 批准号:
1906854 - 财政年份:2019
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
NRI: INT: COLLAB: Program Verification and Synthesis for Collaborative Robots
NRI:INT:COLLAB:协作机器人的程序验证和综合
- 批准号:
1925043 - 财政年份:2019
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
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 - 财政年份:2018
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
EAGER: Representations and Methods for Verifiable Human-Robot Interactions
EAGER:可验证的人机交互的表示和方法
- 批准号:
1651129 - 财政年份:2016
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
NRI/Collaborative Research: Models and Instruments for Integrating Effective Human-Robot Teams into Manufacturing
NRI/协作研究:将有效的人机团队集成到制造中的模型和工具
- 批准号:
1426824 - 财政年份:2014
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
CAREER: Designing Socially Adept Robots
职业:设计社交机器人
- 批准号:
1149970 - 财政年份:2012
- 资助金额:
$ 300万 - 项目类别:
Continuing Grant
HCC: Small: Embodied Mediated Communication in Collaborative Work
HCC:小型:协作工作中的体现中介沟通
- 批准号:
1117652 - 财政年份:2011
- 资助金额:
$ 300万 - 项目类别:
Continuing Grant
HCC: Small: Designing Effective Gaze Mechanisms for Cross-Modal Embodied Agents
HCC:小:为跨模式实体代理设计有效的注视机制
- 批准号:
1017952 - 财政年份:2010
- 资助金额:
$ 300万 - 项目类别:
Continuing Grant
相似海外基金
EMERGENCE: Tackling Frailty - Facilitating the Emergence of Healthcare Robots from Labs into Service
出现:解决脆弱性——促进医疗机器人从实验室进入服务领域
- 批准号:
EP/W000741/1 - 财政年份:2022
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An Outside-the-Box Approach that Integrates Robots into a Company’s Existing Warehouse Infrastructure, Enabling Them to Automate Their Processes at an Approachable Price Point
一种开箱即用的方法,将机器人集成到公司现有的仓库基础设施中,使他们能够以可承受的价格实现流程自动化
- 批准号:
93426 - 财政年份:2021
- 资助金额:
$ 300万 - 项目类别:
Collaborative R&D
Research into resilient under-actuated robots
弹性欠驱动机器人的研究
- 批准号:
RGPIN-2016-06007 - 财政年份:2021
- 资助金额:
$ 300万 - 项目类别:
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Research into Deep Learning for the Control of Concentric Tube Robots and other Continuum Based Robots
同心管机器人和其他连续体机器人控制的深度学习研究
- 批准号:
2338607 - 财政年份:2020
- 资助金额:
$ 300万 - 项目类别:
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Research into resilient under-actuated robots
弹性欠驱动机器人的研究
- 批准号:
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弹性欠驱动机器人的研究
- 批准号:
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- 资助金额:
$ 300万 - 项目类别:
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Research into resilient under-actuated robots
弹性欠驱动机器人的研究
- 批准号:
RGPIN-2016-06007 - 财政年份:2018
- 资助金额:
$ 300万 - 项目类别:
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Research into resilient under-actuated robots
弹性欠驱动机器人的研究
- 批准号:
RGPIN-2016-06007 - 财政年份:2017
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An investigation into novel strategies for adaptive, intelligent and robust control of autonomous robots through the study of dynamic interactions bet
通过动态交互赌注的研究,研究自主机器人自适应、智能和鲁棒控制的新策略
- 批准号:
1803462 - 财政年份:2016
- 资助金额:
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Constraint-based Transformations of Abstract Task Plans into Executable Actions for Autonomous Robots
基于约束的抽象任务计划到自主机器人可执行动作的转换
- 批准号:
288705857 - 财政年份:2016
- 资助金额:
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Research Grants














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