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: 1607299
• 负责人: Sonia Chernova
• 金额: $ 19.39万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1607299&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.

与人合作的机器人的发展对于制造业，医药，医疗保健，军事和消费品等行业至关重要。实现这一目标的关键是开发能够适应不断变化的任务和用户需求的机器人技术。随着可用性需求的变化-无论是由于制造过程的修改，引入新患者还是搬迁到新的家庭-技术技能有限的用户必须能够定制机器人系统的功能。该项目将贡献新的理论模型，技术和开源实现，使用户能够在不编程的情况下有效地将高级任务知识传达给机器人。 智力优点：该项目将利用人机交互来提高对象识别、特征选择和策略学习算法的效率，并开发数据重用和算法评估的新技术。将通过为互联网规模的快速评估和测试开发一个创新的双重现实框架来验证假设。 理论将在两个机器人应用领域进行测试：家庭辅助和制造。该项目的成果将是一个独立于领域的交互式学习框架，能够从与单个非专家用户的简短交互中进行自适应对象识别、特征选择和策略学习。更广泛的影响：这项研究的长期目标是有一天使个人机器人能够为日常生活所用。 这项研究将有助于新的理论模型，技术和开源实现，这将加速与人类一起工作的机器人的开发和采用。由此产生的自适应交互系统的理论和方法将有潜在的社会影响超越机器人技术，在软件系统，如个人电脑和移动的设备。为了促进这一领域的研究，研究人员将把所有开发的软件组件作为开放源码提供。这项研究还将纳入本科和研究生课程，以及为初中和高中学生开展的外联活动。基于网络的评价框架将提供一个独特的机会，通过提供获取尖端机器人技术的机会并使公众有能力为机器人技术的发展作出贡献，来教育公众。