CRII: CHS: Healthcare Professionals Teaching Robots Communication Strategies for Effective Intervention Delivery

在美国，医护人员持续短缺。行为治疗师、护士、理疗师和家庭护理人员等职业是目前和未来医疗保健职业中需求最大的职业。社交辅助机器人是一种新的战略技术，通过扩大我们现有劳动力向有需要的个人提供医疗干预的能力，有可能缓解医疗行业的劳工担忧。然而，由于医疗服务消费者的需求快速变化，通常需要适应现有干预措施或开发新的干预措施，这些机器人技术仍然难以扩展，难以整合到现实世界的医疗干预服务交付中。该项目将为医疗保健专业人员建立使能技术，以便根据他们消费者的需求以及他们的临床环境来生成和定制机器人中介干预。这项开发的技术还将使医疗保健专业人员能够向机器人传授有效干预的沟通策略，以便社交辅助机器人能够像他们的人类老师一样提供同等的干预服务。因此，这项工作将通过缓解医疗行业现有的劳动力担忧而造福社会，并通过与社交辅助机器人合作，使消费者更容易获得和定制医疗服务。这项研究的结果将促进有效的人-机器人通信、机器人学习和人-机器人教学界面的知识。为了实现这些目标，该项目将开发一个受演示启发的学习框架，使没有编程经验的医疗保健专业人员能够教授社交辅助机器人执行有效干预交付所需的干预和非语言沟通策略。将解决以下基本研究挑战：(A)教学界面：研究人员将设计和构建一个社交沉浸式教学界面，使医疗保健专业人员能够遥控社交辅助机器人，以演示干预交付任务和干预期间有效的非语言沟通策略。主要的重点将是确定最有效和最高效的远程操作模式，以从医疗保健专业人员那里获得高质量的演示。(B)从示范中学习干预措施：将设计算法，以从医疗保健专业人员的示范中学习干预交付任务的结构、他们在这些干预措施期间使用的有效非语言交流行为以及使用这些干预措施的背景。这些学习到的模型将被社交辅助机器人用来为医疗服务消费者进行学习的干预。(C)干预过程中的非语言交流：该项目将在一项人-机器人交互研究中达到高潮，该研究的重点是在真实世界干预交付场景中整合和评估机器人中介的干预，该干预利用类似人类的非语言沟通策略。这项研究的结果将促进有效的人-机器人沟通、机器人学习和人-机器人教学界面的知识。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。

There is a persistent shortage of healthcare workers in the USA. Occupations such as behavioral therapists, nurses, physical therapists, and home care workers are among the most in demand present and future healthcare professions. Socially assistive robots are a new strategic technology that has the potential to alleviate labor concerns in the healthcare industry by amplifying the capabilities of our existing labor force in delivering healthcare interventions to individuals in need. However, these robotic technologies remain difficult to scale and challenging to integrate in real-world healthcare intervention delivery due to the rapidly changing needs of healthcare service consumers that often require adaptation of existing interventions or development of new interventions. This project will build enabling technologies for healthcare professionals to generate and customize robot-mediated interventions according to the needs of their consumers as well as their clinical settings. The developed technology will also enable healthcare professionals to teach robots communication strategies for effective intervention delivery so that socially assistive robots can provide on par intervention services as their human teachers. Hence, this work will benefit society by alleviating the existing labor concerns for the healthcare industry and make healthcare services more accessible as well as customizable to consumers via collaboration with socially assistive robots. The outcomes of this research will advance knowledge in effective human-robot communication, robot learning, and human-robot teaching interfaces.To address these goals, this project will develop a Learning from Demonstration inspired framework that will enable healthcare professionals with no prior experience in programming to teach socially assistive robots to administer intervention and nonverbal communication strategies necessary for effective intervention delivery. The following fundamental research challenges will be addressed: (a)Teaching Interface: The researchers will design and build a socially immersive teaching interface which enables healthcare professionals to teleoperate a socially assistive robot to demonstrate intervention delivery tasks and effective nonverbal communication strategies during the intervention. The primary focus will be identifying teleoperation modalities that are most effective and efficient for attaining high-quality demonstrations from healthcare professionals. (b) Learning Interventions from Demonstrations: Algorithms will be designed to learn from demonstrations by healthcare professionals’ models of the structure of an intervention delivery task, effective nonverbal communication behaviors they use during these interventions, and the context of their use. These learned models will then be utilized by a socially assistive robot to carry out the learned interventions for healthcare service consumers.(c) Nonverbal Communication during Interventions: The project will culminate in a human-robot interaction study focusing on integrating and evaluating robot-mediated interventions, which utilize human-like nonverbal communication strategies, in real-world intervention delivery scenarios.The outcomes of this research will advance knowledge in effective human-robot communication, robot learning, and human-robot teaching interfaces.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.