NRI: INT: Customizing Semi-Autonomous Nursing Robots Using Human Expertise

NRI：INT：利用人类专业知识定制半自主护理机器人

• 批准号: 2025782
• 负责人: Kris Hauser
• 金额: $ 70.11万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025782&HistoricalAwards=false
• 关键词: NRI; INT; Customizing; Semi; Autonomous

Remote-controlled robots have the potential to allow humans to perform useful tasks without putting themselves in danger, or without travelling long distances. This project explores how humans can control nursing robots that can communicate with patients, collect vital signs, and perform routine cleaning tasks in quarantine environments. The use of these robots has the potential to protect nurses from infection during disease outbreaks, and to protect patients with weakened immune system. A significant challenge in this effort is to make the user interface to the robot easy enough for nurses to use without significant training. Because engineers are not experts in nursing, the research will let nurses customize the user interface by teaching the robot about objects, places, and tasks that are typically used in nursing. After training, artificial intelligence algorithms will then automatically estimate which actions the nurse wants to perform, and these will be presented in a simple user interface that allows the nurse to select those actions quickly. This project will continue an interdisciplinary collaboration between Duke's School of Engineering and School of Nursing. Research will be conducted in three thrust areas: 1) smart human operator interfaces for supervised autonomy that learn mappings between multimodal sensor input streams to provide simple, interpretable task options and status feedback; 2) hierarchical task learning algorithms for helping human experts train novel composite tasks; and 3) real world evaluation of human-robot system speed, reliability, operator workload, and operator learning curve using registered nurses and nursing students performing simulated clinical tasks in training environments.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.

遥控机器人有潜力让人类执行有用的任务，而无需将自己置于危险之中，也无需长距离旅行。该项目探讨了人类如何控制护理机器人，使其能够与患者沟通、收集生命体征并在隔离环境中执行日常清洁任务。这些机器人的使用有可能保护护士在疾病爆发期间免受感染，并保护免疫系统较弱的患者。这项工作的一个重大挑战是让机器人的用户界面足够简单，让护士无需经过大量培训即可使用。由于工程师不是护理方面的专家，因此该研究将让护士通过向机器人传授护理中通常使用的物体、地点和任务来定制用户界面。训练后，人工智能算法将自动估计护士想要执行哪些操作，这些操作将显示在一个简单的用户界面中，使护士可以快速选择这些操作。该项目将继续杜克大学工程学院和护理学院之间的跨学科合作。研究将在三个重点领域进行：1）用于监督自主的智能人类操作员界面，学习多模式传感器输入流之间的映射，以提供简单、可解释的任务选项和状态反馈； 2）分层任务学习算法，帮助人类专家训练新颖的复合任务； 3) 使用注册护士和护理学生在培训环境中执行模拟临床任务，对人机系统速度、可靠性、操作员工作量和操作员学习曲线进行真实世界评估。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：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。

项目成果

Automated Heart and Lung Auscultation in Robotic Physical Examinations

机器人体检中的自动心肺听诊

• DOI: 10.1109/lra.2022.3149576
• 发表时间: 2022-01-24
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Yifan Zhu;A. Smith;Kris K. Hauser
• 通讯作者: Kris K. Hauser

Real-time Semantic 3D Reconstruction for High-Touch Surface Recognition for Robotic Disinfection

用于机器人消毒的高接触表面识别的实时语义 3D 重建

• 发表时间: 2022-10
• 期刊: Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
• 作者: Qiu, Ri;Sun, Yixiao;Marques, Joao Marcos;Hauser, Kris
• 通讯作者: Hauser, Kris

Structured Action Prediction for Teleoperation in Open Worlds

开放世界中远程操作的结构化动作预测

• DOI: 10.1109/lra.2022.3145953
• 发表时间: 2022-04
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Naughton, Patrick;Hauser, Kris
• 通讯作者: Hauser, Kris

Immersive Commodity Telepresence with the AVATRINA Robot Avatar

AVAVARINA 机器人头像带来沉浸式商品远程呈现

• DOI: 10.1007/s12369-023-01090-1
• 发表时间: 2024-01
• 期刊: International Journal of Social Robotics
• 影响因子: 4.7
• 作者: Correia Marques, Joao Marcos;Naughton, Patrick;Peng, Jing;Zhu, Yifan;Nam, James Seungbum;Kong, Qianxi;Zhang, Xuanpu;Penmetcha, Aman;Ji, Ruifan;Fu, Nairen;et al
• 通讯作者: et al

Semi-Empirical Simulation of Learned Force Response Models for Heterogeneous Elastic Objects

非均质弹性物体习得力响应模型的半经验模拟

• 发表时间: 2020-05
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Zhu, Yifan;Lu, Kai;Hauser, Kris
• 通讯作者: Hauser, Kris