EAGER: Principles of Motor Skills (re)Learning When Using Powered Exoskeletons

外骨骼具有巨大的潜力，可以在动态的工业环境中增强体力，同时保持人类的技能。然而，许多涉及工具的工业任务是复杂的，需要高度灵活的控制。工人也有不同的经验水平，拥有不同的身体和认知技能。因此，外骨骼的可接受性和适当的培训是决定未来工业应用的关键因素。这一早期概念探索性研究资助(AGIRE)项目将通过确定使用多关节手臂外骨骼执行复杂任务时学习人类运动技能的基本原则，促进科学进步，促进国家健康、繁荣和福祉。通过向具有不同任务相关技能的基线水平的用户介绍外骨骼，并通过使用一套全面的神经运动测量对学习进行建模，该项目有望发现指导设计适应性外骨骼控制算法和个性化培训协议的基本原则，这些外骨骼和具有不同技能的个人之间可以产生真正的协作。这项工作的更广泛影响包括举办讲习班，重点是提高工人对外骨骼及其在工作场所的潜在应用的认识，以及利用外骨骼进行工人技能培训的机会。这项工作将通过系统地操纵影响人与机器之间协作的关键心理-运动-机器因素，促进对人-外骨骼系统中出现的复杂学习动态的了解。我们的新贡献将是通过研究不同技能和不同复杂任务的广泛用户使用外骨骼的短期和长期适应，扩展运动适应的两阶段多速率模型的普适性。持续激励的概念将被用来扰动人类完成任务的动态条件，以最大限度地从机器中学习，而极值搜索控制方法将被实施，以使机器从人类的反应中学习。这两个概念将被结合在一个新的外骨骼控制算法中，随着人类外骨骼使用的演变，外骨骼参数(如扭矩放大)将被自适应地调整。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。

Exoskeletons have great potential to augment physical power while preserving human skill in dynamic industrial environments. However, many industrial tasks involving tools are complex, and require high dexterous control. Workers also have different levels of experience and possess different physical and cognitive skill-sets. Hence, exoskeleton acceptability and appropriate training are key factors that will determine future industrial applications. This EArly-concept Grant for Exploratory Research (EAGER) project will promote the progress of science and advance the national health, prosperity and welfare by identifying basic principles of human motor skill learning when using multi-joint arm exoskeletons to perform complex tasks. By introducing exoskeletons to users with varying levels of task-relevant skills at baseline, and by modeling learning using a comprehensive set of neuromotor measures, the project promises to discover fundamental principles that will guide the design of adaptable exoskeleton control algorithms and personalized training protocols that can produce true collaboration between exoskeletons and individuals with diverse skills. Broader impacts of the work include workshops focused on increasing workers awareness of exoskeletons and their potential applications in the workplace, as well as opportunities for worker-skills training using exoskeletons.This work will advance knowledge of the complex learning dynamics that arise in human-exoskeleton systems through systematic manipulation of critical mind-motor-machine factors influencing the collaboration between man and machine. Our novel contribution will be to expand the generalizability of a two-stage multi-rate model of motor adaptation by studying short- and long-term adaptations with exoskeleton use by a broad range of users with varying skill and tasks of varying complexity. The concept of persistent excitation will be leveraged to perturb the dynamical conditions under which the human accomplishes the task, to maximize human learning from the machine, while an extremum seeking control approach will be implemented for the machine to learn from the human response. The two concepts will be combined in a novel exoskeleton control algorithm for adaptively tuning exoskeleton parameters (such as torque amplification) as the human’s use of the exoskeleton evolves.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.