NRI: Small: Understanding neuromuscular adaptations in human-robot physical interaction for adaptive robot co-workers

该奖项的目标是开发理论、方法和工具，以了解人类-机器人物理交互中神经运动适应的机制。人力辅助系统，例如帮助操作员操纵重载或重载的电动起重装置，需要操作员和机器之间的物理接触，从而创建一个耦合的动力系统。这种耦合的动力学已被证明引入了固有的不稳定性和由于人的刚度的变化而导致的性能下降；当遇到不稳定性时，人类操作员通常试图通过使手臂僵硬来控制振荡，这导致系统更加僵硬，具有更多的不稳定性。该项目将为机器人同事建立控制算法，主动调整操作员和机器人操作器之间的接触阻抗，以实现更高的性能和稳定性。这项研究将1)了解神经肌肉适应和系统性能极限之间的联系，2)开发概率方法来分类和预测操作员的认知和物理状态从生理测量的转变，以及3)将这些知识集成到共享的人-机器人的结构中，并在车辆装配设施的真实世界约束的情况下展示其在动力提升装置中的有效性。如果成功，这项研究将使对先进制造和工艺设计(包括汽车、航空航天和军事)的自适应共享控制方法感兴趣的社区受益。这种下一代制造预计将提高生产率，减少装配时间，并减少装配线工人的身体负担。研究成果将纳入研究生和本科生的当前课程。学生将从跨学科和多文化群体中招收，包括代表性不足的群体。将与佐治亚理工学院学生和教师增强伙伴计划以及当地非营利性协会的机器人夏令营一起开展K-12外联活动。维护了一个在线门户网站以供传播。

The goal of this award is to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction. Human power-assisting systems, e.g., powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, creating a coupled dynamic system. This coupled dynamic has been shown to introduce inherent instabilities and performance degradation due to a change in human stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability. The project will establish control algorithms for robot co-workers that proactively adjust the contact impedance between the operator and robotic manipulator for achieving higher performance and stability. This research will 1) understand the association between neuromuscular adaptations and system performance limits, 2) develop probabilistic methods to classify and predict the transition of operator's cognitive and physical states from physiological measures, and 3) integrate this knowledge into a structure of shared human-robot and demonstrate the efficacy in a powered lifting device with real-world constraints at vehicle assembly facilities. If successful, the research will benefit the communities interested in the adaptive shared control approach for advanced manufacturing and process design, including automobile, aerospace, and military. Such next-generation manufacturing is expected to improve productivity and reduce assembly time as well as physical burden of assembly line workers. Research outcomes will be integrated into current courses at both graduate and undergraduate levels. Students will be recruited from interdisciplinary and multicultural groups including under-represented groups. K-12 outreach will be carried out in conjunction with Georgia Tech Student and Teacher Enhancement Partnership Program and a summer robot camp in a local non-profit association. An online portal is maintained for dissemination.