NRI: INT: Learning-Enabled Robot Support of Daily Activities for Successful Activity Completion

NRI：INT：支持学习的机器人支持日常活动以成功完成活动

• 批准号: 1734558
• 负责人: Diane Cook
• 金额: $ 100万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734558&HistoricalAwards=false
• 关键词: NRI; INT; Learning; Enabled; Robot

The population is aging - the estimated number of individuals over the age of 85 will triple by 2050. Even in our current population an estimated 9% of adults age 65+ and 50% of adults age 85+ need assistance with everyday activities, and the annual cost for the United States is roughly $2 trillion. Given the economic and quality of life costs, there is a critical need to better use smart technologies so that individuals can live independently in their own homes, helping both individuals and society as a whole. This proposed work will create a novel multi-agent robot system, called Robotic Activity Support (RAS), that provides in-home activity support for older adults and others that need assistance to independently perform common activities of daily living. The system will rely on cooperation between a smart home and a mobile robot to learn activity routines for an individual. RAS will use this information to provide activity interventions that help smart home residents initiate and successfully complete important daily activities and improve functional independence. Rather than explore co-robot systems with multiple identical platforms, the system will represent a collaboration between a mobile robot, a smart home agent with multiple heterogeneous sensors and multiple humans with distinct roles. For this collaboration, the project team will employ a custom robot that will partner with the team's CASAS smart home architecture. RAS will incorporate caregiver-in-the-loop active learning to improve its models. the team will use an iterative user-centered development process to enhance the mobile robotic platform design. The RAS system will use active learning from both the resident and caregiver to learn common activities and how it can support such activities. For example, the robot may prompt a resident to eat breakfast if she does not initiate the task at the normal time, remind the resident where the cereal is, and notify a nearby caregiver if help is needed that is beyond the robot's capabilities. The team will evaluate RAS on historic smart home data (from their more than 100 deployments), in their on-campus smart home, and in a home with a healthy older adult caregiver, as well as an older adult exhibiting cognitive limitations.

人口正在老龄化 - 到2050年，估计85岁以上的个体数量将三倍。即使在我们目前的人口中，估计有9％的成年人年龄在65岁以上和50％的成年人中，有85岁以上的成年人需要帮助日常活动，而美国的年度费用约为2万美元。鉴于生活成本的经济和质量，至关重要的需要更好地使用智能技术，以便个人可以独立生活在自己的家中，从而帮助个人和整个社会。这项拟议的工作将创建一个新型的多代理机器人系统，称为机器人活动支持（RAS），该系统为老年人和需要帮助独立执行日常生活的共同活动的其他人提供家庭活动支持。该系统将依靠智能家庭和移动机器人之间的合作来学习个人的活动例程。 RAS将使用此信息提供活动干预措施，以帮助智能家庭居民发起并成功完成重要的日常活动并改善功能独立性。该系统并没有探索具有多个相同平台的共同机器人系统，而是代表移动机器人，具有多个异构传感器的智能家居经纪人和多个具有独特角色的人之间的协作。为了进行这项合作，项目团队将采用一个定制机器人，该机器人将与团队的Casas Smart Home Architecture合作。 RAS将结合护理人员在环上学习以改善其模型。该团队将使用以用户为中心的迭代开发过程来增强移动机器人平台设计。 RAS系统将使用居民和照料者的积极学习来学习常见的活动以及如何支持此类活动。例如，机器人可能会促使居民吃早餐，如果她不在正常时间启动任务，请提醒居民谷物所在的地方，并在需要帮助的情况下通知附近的护理人员，这超出了机器人的能力。该团队将在其校园内的智能家居中评估RAS的历史性智能家居数据（从其100多个部署中），以及具有健康的老年人护理人员的家庭，以及具有认知限制的老年人。

项目成果

Robot-enabled support of daily activities in smart home environments

• DOI: 10.1016/j.cogsys.2018.10.032
• 发表时间: 2019-05-01
• 期刊: COGNITIVE SYSTEMS RESEARCH
• 影响因子: 3.9
• 作者: Wilson, Garrett;Pereyda, Christopher;Cook, Diane J.
• 通讯作者: Cook, Diane J.