Reshaping Motor Learning in High-Dimensional Tasks via Soft Robotic Physical Interactions

在美国，中风等神经损伤后的运动障碍是导致长期残疾的主要原因之一。上肢的损伤
，包括手和手指的功能
，是非常常见的，经常导致进行
日常活动的困难。
神经康复通常需要学习协调模式，涉及身体中大量关节的复杂运动。由于指令和视觉反馈等常见方法在指导这种复杂的运动学习方面基本上是无效的，因此该项目旨在通过使用机器人施加力来理解和促进复杂协调模式的学习。该项目将导致软体机器人手套和相关算法的发展，以理解和促进运动学习。了解如何在复杂任务中指导运动学习在中风康复、运动员技能训练、人机协作操作和机器人辅助手术等方面具有广泛的应用。因此，该项目的进展将在这些领域产生重大的社会影响。这项多学科研究与外展和教育活动相结合，旨在扩大未被充分代表的群体的参与，并增加初高中学生对工程研究的参与。利用团队在运动学习、计算建模、机器人和控制方面的独特和互补的专业知识，该研究项目将形成一个严谨、系统的框架，用于建模和促进双向人机学习，通过物理交互，用于涉及大量自由度的任务。该项目的目标将通过四个综合研究重点来实现：(1)开发一种柔软、紧凑、传感器丰富的机器人手套，可以对单个手指关节施加所需的辅助或阻力（模拟损伤）；(2)开发基于动态模型分解（DMD）的数据驱动模型，捕捉学习动态和机器人辅助/阻力的作用；(3)利用基于dmd的模型和模型预测控制理论设计所需的机器人辅助和目标位置序列，促进快速学习；(4)通过对任务表现的演变和手部关节的潜在协调模式的研究，通过大量的实验来评估研究方法。运动学习将在一个运动协调任务中进行评估，在这个任务中，手关节的运动将映射到屏幕上光标的上、下、右、左运动。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。

Movement impairments after neurological injury such as stroke are one of the leading causes of long term disability in the United States. Impairments
 of the upper extremity, including hand and finger
function, are extremely common, often resulting in difficulties performing
daily activities. 
Neurorehabilitation often requires learning coordination patterns that involve the complex motion of a large number of joints in the body. Because common approaches such as instructions and visual feedback are largely ineffective for guiding such complex motor learning, this project aims to understand and facilitate the learning of complex coordination patterns by applying forces using robots. The project will lead to development of a soft-robotic glove and associated algorithms for understanding and facilitating motor learning. Understanding how to guide motor learning in complex tasks has a wide range of applications in stroke rehabilitation, skill-training for athletes, collaborative human-robot manipulation, and robot-assisted surgery among others. Advances made in this project will thus have significant societal impact in these areas. The multi-disciplinary research is integrated with outreach and educational activities that aim to broaden the participation of underrepresented groups and to increase involvement of middle and high school students in engineering research. Drawing on unique and complementary expertise of the team in motor learning, computational modeling, robotics, and control, this research project will result in a rigorous, systematic framework for modeling and facilitating bidirectional human-robot learning, via physical interactions, for tasks involving a large number of degrees of freedoms. The project goal will be achieved through four integrated research thrusts: (1) developing a soft, compact, and sensor-rich robotic glove that can apply desired assistance or resistance (emulating impairment) to individual finger joints; (2) developing dynamic model decomposition (DMD)-based data-driven models that capture the learning dynamics and the role of robotic assistance/resistance; (3) exploiting the DMD-based models and model predictive control theory to design the desired robotic assistance as well as the sequence of target locations, to facilitate rapid learning; and (4) evaluating the research approach with extensive experiments, by examining the evolution of the task performance and the underlying coordination patterns of hand joints. The motor learning will be evaluated on in a motion coordination task in which motion of the joints of the hand will be mapped to the up, down, right and left motion of a cursor on a screen.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.