NRI: FND: The Robotic Rehab Gym: Specialized co-robot trainers working with multiple human trainees for optimal learning outcomes

该奖项将支持那些将获得关于机器人群体如何有效地向人类群体传授不同技能的新基础知识的研究。虽然成群结队的机器人已经可以帮助成群结队的人类，但现有的研究主要集中在短期的性能增强和/或安全和健康上。相反，这个项目将推进“机器人健身房”的概念：多个专门的机器人教练向人类团体教授多种不同的技能，每个机器人教练专注于不同的技能，每个人类学习者具有不同的初始能力和不同的改进潜力。这代表了一个具有重大不确定性的长期规划问题（特别是因为当前的性能不能总是预测未来的改进），并且将通过先进的人工智能技术来解决，这些技术将在模拟和实际的人类受试者研究中进行评估。研究结果将在许多方面促进国民健康和福祉。它们可以直接用于运动康复、运动和外科手术的机器人辅助训练；此外，它们可以广泛应用于任何机器用于训练人类群体的环境，包括教育和减肥。该团队还将开发新的人机交互跨学科课程，并将向多个群体，特别是怀俄明州的K-12和社区大学的学生和教师进行机器人技术的推广。该项目将分为三个互补的工作包，将开发拟议中的“机器人健身房”的三个要素——一个多机器人系统，向人类群体传授多种技能。第一个工作包将创建一个智能系统，该系统动态地将多个人类受训者分配给多个机器人训练员，目标是最大化个人和团体训练结果。第二个工作包将创建基于当前和过去绩效测量的预测未来培训结果的方法，这可以作为智能分配系统决策的基础。最后，第三个工作包将创建一个人机协作规划框架，允许机器人健身房从人类专家（如治疗师）那里学习训练策略，并为人类专家提供决策支持，从而结合人类和机器智能的互补优势。在整个项目中，机器人健身房将以两种方式进行评估。首先，创建的软件将在许多不同变量的模拟中进行评估（例如，机器人和人类的数量，当前性能的不确定性程度），为研究团体提供有关不同场景下预期有效性的详细信息。其次，它将被用于使用怀俄明大学提供的多个机器人向人类参与者教授多种具有挑战性的手臂动作，为未来的应用研究提供可行性证明。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will support research that will obtain new fundamental knowledge about how groups of robots can effectively teach different skills to groups of humans. While groups of robots can already assist groups of humans, existing research has mainly focused on short-term performance enhancement and/or safety and health. Instead, this project will advance the concept of a "robotic gym": multiple specialized robotic trainers that teach multiple different skills to groups of humans, with each robotic trainer focusing on different skills and each human learner having different initial abilities and different potential for improvement. This represents a long-term planning problem with significant uncertainty (especially since current performance cannot always predict future improvement), and will be addressed with advanced artificial intelligence techniques that will be evaluated both in simulations and in actual human subjects studies. Results of the research will advance national health and wellbeing in many ways. They can most directly be used for robot-aided training in motor rehabilitation, sports, and surgery; furthermore, they can be broadly applied to any setting where machines are used to train groups of humans, including education and weight loss. The team will also develop new interdisciplinary courses in human-robot interaction, and will perform outreach about robotics to multiple groups, especially K-12 and community college students and teachers around Wyoming.The project will be divided into three complementary work packages that will develop three elements of a proposed "robotic gym" - a multi-robot system that teaches multiple skills to groups of humans. The first work package will create an intelligent system that dynamically allocates multiple human trainees to multiple robotic trainers with the goal of maximizing individual and group training outcomes. The second work package will create ways of predicting future training outcome based on measurements of current and past performance, which can be used as a basis for decision-making by the intelligent allocation system. Finally, the third work package will create a collaborative human-robot planning framework that allows the robotic gym to learn training strategies from human experts (e.g., therapists) and to provide decision support to human experts, thus combining the complementary advantages of human and machine intelligence. The robotic gym will be evaluated in two ways throughout the project. First, the created software will be evaluated in simulations with many different variables (e.g., number of robots and humans, degree of uncertainty about current performance), providing the research community with detailed information about expected effectiveness in different scenarios. Second, it will be used to teach multiple challenging arm motions to groups of human participants using multiple robots available at the University of Wyoming, providing proof of feasibility for future applied research.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.