Collaborative Research: NRI: INT: Transparent and intuitive teleoperation interfaces for the future nursing robots and workers

最近的大流行爆发，包括埃博拉病毒、寨卡病毒和2019年新型冠状病毒(2019-nCoV)，促使远程医疗超越单纯的远程在场，实现机器人执行真实世界的护理辅助任务，这些任务需要协调控制操纵、移动和主动远程操作。远程控制护理机器人为隔离和远程病人护理提供了一种很有前途的替代方案。然而，传统和现代的人-机器人界面从根本上限制了护理机器人遥操作的性能和用户体验，并可能加剧负担和安全担忧，阻碍医护人员采用机器人。为了解决这一问题，本项目将(1)开发透明和直观的远程操作界面的创新集成，以支持远程护理机器人的自由形式和协调运动控制，以及(2)将该界面与机器人智能集成，使护理专业人员能够以最少的培训学习机器人远程操作，并使用共享自主性来减少体力和认知工作量。拟议的项目将促进机器人遥操作人-机器人接口的科学进步，并在当前和未来的大流行危机中提高医疗保健的质量、可用性和可持续性。该项目将对护理领域产生重大影响，该领域由全美290万名注册护士和16万名护士从业人员组成。它将彻底改变隔离中的病人护理，并有可能扩展到家庭护理、诊所和医院，因为即将到来的护理劳动力短缺。基础研究还将推广到其他具有机器人遥操作的工人领域，包括仓库、社会服务和维护。这项拟议的研究将在机器人工程、护理和社会科学领域促进教职员工和学生之间的实质性合作。本项目由两个研究主题组成。研究主题1将开发一个软机器人遥操作接口体系结构和系统的人类启发的运动映射策略，以支持人类和机器人之间直观和透明的运动、力和感知信息的映射。建议的界面将使机器人能够透明和清晰地进行伸手抓握、机器人操纵和主动远程临场感控制。研究主题2将开发界面的智能，以实现人类和机器人之间的互动学习和相互适应。基于博弈论规划，它将开发自适应共享自主策略，通过触觉反馈使用人与机器人的交流。它将采用主动远程呈现，以加强培训并减少人工操作员的远程操作工作量。综合界面将在与注册护士、护理教师和护理学生进行的综合用户研究中进行评估。评估将评估性能和用户体验，包括人-机器人合作、使用效率、工作量和界面工作指标。它还将评估拟议的人-机器人接口对当前和未来护理劳动力接受和采用护理机器人的社会影响。这项提议是由国家职业安全与健康研究所(NIOSH)在疾病控制和预防中心(CDC)资助的。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The recent pandemic outbreaks, including Ebola, Zika and the 2019 Novel Coronavirus (2019-nCoV), urge tele-medicine to go beyond mere tele-presence, to achieve robots that perform real-world nursing assistance tasks that require the coordinated control of manipulation, locomotion, and active teleoperation. Remotely-controlled nursing robots provide a promising alternative for quarantine and remote patient care. However, the traditional and contemporary human-robot interfaces fundamentally limit the performance and user experience of nursing robot teleoperation, and may reinforce burden and safety concerns that discourage healthcare workers to adopt robots. To address this problem, this project will (1) develop an innovative integration of transparent and intuitive teleoperation interface, to support the freeform and coordinated motion control of the remote nursing robots, and (2) integrate this interface with the robot intelligence to enable nursing professionals to learn robot teleoperation with minimal training, and to reduce the physical and cognitive workload using shared autonomy. The proposed project will promote the progress of science in human-robot interfaces for robot teleoperation, and advance the quality, availability and sustainability of healthcare in the present and future pandemic crisis. This project will have significant impacts on the domain of nursing, which consists of 2.9 million registered nurses and 160,000 nurse practitioners across the U.S. It will revolutionize patient-care in quarantine, and has the potential to extend to in-home care, clinics, and hospitals given the upcoming shortage of nursing workforce. The fundamental research also generalizes to other worker domains with robot tele-operations, including warehouse, social service, and maintenance. The proposed research will forge substantial collaboration among faculty and students in robotics engineering, nursing and social science. This project consists of two research themes. Research Theme 1 will develop a soft-robot teleoperation interface architecture and systematic human-inspired motion mapping strategies, to support the intuitive and transparent mapping of the motion, force, and perception information between humans and robots. The proposed interface will enable transparent and legible robot behavior of reaching-to-grasp, loco-manipulation, and the control of active telepresence. Research Theme 2 will develop the intelligence of the interface, to enable interactive learning and mutual adaptation between humans and robots. Based on game-theoretic planning, it will develop adaptive shared autonomous strategies that use human-robot communication via haptic feedback. It will employ active tele-presence to enhance the training and reduce workload in tele-operation of the human operator. The integrated interface will be evaluated in comprehensive user studies with registered nurses, nursing faculty and nursing students. The evaluation will assess the performance and user experience, including human-robot teaming, using efficiency, workload and interface effort metrics. It will also evaluate the social impacts of the proposed human-robot interface on the acceptance and adoption of nursing robots by the current and future nursing workforce. This proposal was funded with the National Institute for Occupational Safety and Health (NIOSH) in the Center for Disease Control and Prevention (CDC).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.