CAREER: Towards Robots that Learn from Everyday Users

职业生涯：向日常用户学习的机器人

• 批准号: 1149876
• 负责人: Sonia Chernova
• 金额: $ 49.95万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149876&HistoricalAwards=false
• 关键词: CAREER; Towards; Robots; Learn; Everyday

The development of robots that work cooperatively with people is of critical importance for industries as diverse as manufacturing, medicine, healthcare, military, and consumer products. Key to this goal is the development of robotic technologies that are adaptable to changing task and user needs. As usability demands change - whether due to modifications in the manufacturing process, the introduction of a new patient, or the relocation to a new home - users with limited technical skills must have the ability to customize the functionality of robotic systems. This project will contribute new theoretical models, techniques and open source implementations that will enable users to effectively communicate high level task knowledge to robots without programming. Intellectual merit: The project will leverage human-robot interaction to improve the efficiency of object recognition, feature selection and policy learning algorithms, as well as develop new techniques for data reuse and algorithm evaluation. Hypotheses will be validated through the development of an innovative dual-reality framework for Internet-scale rapid evaluation and testing. Theories will be tested in two robotic applications areas: home assistance and manufacturing. The outcome of this project will be a domain independent interactive learning framework capable of adaptive object recognition, feature selection and policy learning from brief interactions with a single, non-expert user. Broader impact: The long term goal of this research is to one day make personal robots accessible to everyday people. This research will contribute new theoretical models, techniques and open source implementations that will accelerate the development and adoption of robots that work alongside people. The resulting theories and methodologies for adaptive interactive systems will have potential societal impact beyond robotics, in software systems such as personal computers and mobile devices. To promote research in this area, the investigator will make all developed software components available as open source. The research will also be integrated into courses at the undergraduate and graduate levels, as well as into outreach activities for middle and high school students. The web-based evaluation framework will provide a unique opportunity to educate the general public by providing access to cutting-edge robotic technology and empowering them to contribute to its development.

与人合作合作的机器人的开发对于制造，医学，医疗保健，军事和消费产品等多样化的行业至关重要。这个目标的关键是开发适合不断变化的任务和用户需求的机器人技术。随着可用性的要求变化 - 无论是由于制造过程中的修改，新患者的引入还是搬迁到新房屋中，技术技能有限的用户都必须具有自定义机器人系统功能的能力。该项目将贡献新的理论模型，技术和开源实现，这将使用户无需编程即可有效地将高级任务知识传达给机器人。 智力优点：该项目将利用人类机器人的互动来提高对象识别，特征选择和政策学习算法的效率，并开发用于数据重用和算法评估的新技术。假设将通过开发创新的双重现实框架来验证，用于互联网规模的快速评估和测试。 理论将在两个机器人应用领域进行测试：家庭援助和制造。该项目的结果将是一个独立的互动学习框架，能够从与单个非专家用户的简短互动中自适应对象识别，功能选择和策略学习。更广泛的影响：这项研究的长期目标是有一天使每天的人都可以使用个人机器人。 这项研究将贡献新的理论模型，技术和开源实施，这些模型将加快与人一起使用的机器人的开发和采用。自适应互动系统的最终理论和方法论将在个人计算机和移动设备等软件系统中具有潜在的社会影响。为了促进该领域的研究，研究人员将使所有开发的软件组件可作为开源。该研究还将纳入本科和研究生级别的课程，以及中学和高中生的外展活动。基于Web的评估框架将通过提供对尖端机器人技术的访问并授权他们为其发展做出贡献，为公众提供独特的机会。