CAREER: Democratizing Robot Learning for Assistive Robotics in MCI

职业：MCI 辅助机器人的机器人学习民主化

• 批准号: 2340177
• 负责人: Matthew Gombolay
• 金额: $ 59.98万
• 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: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340177&HistoricalAwards=false
• 关键词: CAREER; Democratizing; Robot; Learning; Assistive

The US population is aging, and 18 percent of adults over 60 years of age have Mild Cognitive Impairment (MCI). Of those with MCI, up to 15 percent develop dementia down the line. Unfortunately, there is an increased shortage of healthcare workers, and the cost of at-home nursing care is prohibitive. To meet this challenge, this Faculty Early Career Development (CAREER) project aims to democratize interactive robot learning to enable care partners (e.g., spouses, children, nurses) to program and personalize a robot's behavior through intuitive modes of interaction to assist the care member (i.e., person with MCI) with activities of daily living. Robot learning is achieved using Learning from Demonstration (LfD), which is about computational methods and interfaces to enable end-users to teach robots new skills through interaction (e.g., skill demonstration). Traditional robotics deployments typically rely on a large number of experts and create robots that are expensive and not easily adapted. Instead, LfD offers a scalable alternative by leveraging efficient algorithms and interactions with non-expert end users. Despite its potential, and decades of research, LfD has not been deployed broadly, in part because these systems do not provide the care partner end-users with insights into the robot's understanding of the world or how the users can be better teachers. This award seeks to overcome the limitations of traditional robotics (such as cost and scale) and modern robot learning approaches (the fact that they are relegated to the laboratory) by enabling robot learning to be accessible and scalable to support aging in place for persons with MCI. The eventual goal is to have a robot that can be used in the home of someone with MCI to help them with their daily tasks, and that the robot can be easily programmed by someone who does not have specific technical training.To achieve these goals, the research team will develop new LfD algorithms and interfaces in partnership with collaborators at the Cognitive Empowerment Program at Emory University, members of the MCI community, clinicians, and researchers at the Georgia Institute of Technology in a transdisciplinary research effort. With the oversight and input from MCI focus groups, the research team will collaboratively execute three research thrusts. First, the team will conduct transdisciplinary research to collect and open-source a first-of-its-kind, multimodal, longitudinal dataset from care partners interacting with a robot with the goal of programming it via LfD to carry out assistive tasks for care members. Second, the team will formulate novel, explainable Artificial Intelligence techniques enabling users to gain a “theory of mind” of the robot, specifically to foster users gaining insight into the behavior of the robot while learning to collaborate via mixed-initiative LfD interactions. Third, the team will develop novel, peer-teaching LfD algorithms that enables the robot learner to develop a theory of mind about an LfD teacher (i.e. the care partner alongside the person with MCI), leverage that insight to tutor a human teacher, and provide explicit feedback for how the teacher can provide better instruction to the robot. The success of the techniques developed will be based upon improving the robot’s performance at specific tasks, reducing the amount of time required for the user to train the robot, and improving user experience in terms of workload, stress, and perceived usability of the robot. The outcome of this research will be open-source datasets, interfaces, and roadmaps guiding researchers in democratizing robots, transitioning LfD from the laboratory to the real world. Finally, the research team will also develop a new educational outreach program in partnership with high school educators to integrate robotics into their classrooms in underserved communities in Georgia.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.

美国人口正在衰老，60岁以上的成年人中有18％的认知障碍（MCI）。在有MCI的人中，高达15％的发展痴呆症。不幸的是，医疗工作者的短缺不足，禁止在家护理的费用。为了应对这一挑战，这一教师早期职业发展（职业）项目旨在使互动机器人学习民主化，以使护理伙伴（例如配偶，子女，孩子，护士）能够通过直觉的互动方式对机器人的行为进行编程和个性化，以帮助护理人员（即MCI）进行日常生活活动。机器人学习是使用从演示中学习（LFD）来实现的，该学习是关于计算方法和接口，以使最终用户能够通过互动来教机器人新技能（例如，技能演示）。传统的机器人部署通常依赖大量专家，并创建昂贵且不容易适应的机器人。相反，LFD通过利用有效的算法和与非专家最终用户的交互来提供可扩展的替代方案。尽管具有潜力，而且还没有大量研究，但并未大致部署LFD，部分原因是这些系统并未为护理伙伴最终用户提供对机器人对世界的理解或用户如何成为更好老师的见解。奖项旨在通过使机器人学习能够访问和扩展，以支持MCI患者的衰老，以克服传统机器人技术（例如成本和规模）和现代机器人学习方法（与实验室相关的事实）的局限性。最终的目标是拥有一个可以在MCI的人的家中使用的机器人，以帮助他们完成日常任务，并且机器人可以通过没有特定技术培训的人轻松地编程。跨学科研究工作中的技术。随着MCI焦点小组的监督和意见，研究团队将协作执行三项研究。首先，该团队将进行跨学科研究，以收集和开放源代码，由护理合作伙伴与与机器人进行交互的首个，多模式的纵向数据集，目的是通过LFD对其进行编程，以执行护理成员的辅助任务。其次，该团队将制定可解释的人工智能技术，使用户能够获得机器人的“心理理论”，特别是为了培养用户在学习通过混合定位性LFD交互进行协作的同时了解机器人的行为。第三，该团队将开发小说，教学的LFD算法，使机器人学习者能够开发有关LFD老师的心态（即，与MCI的人一起护理伙伴），利用这种洞察力来辅导人类老师，并为如何为老师提供更好的教师向机器人提供明确的反馈。开发的技术的成功将基于改善机器人在特定任务上的性能，减少用户训练机器人所需的时间，并在工作量，压力和可感知机器人的可用性方面改善用户体验。这项研究的结果将是开源数据集，界面和路线图指导研究人员将机器人民主化，从实验室过渡到现实世界。最后，研究团队还将与高中教育者合作制定一项新的教育外展计划，以将机器人技术纳入佐治亚州服务不足的社区的教室中。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。