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

该奖项的目标是发展理论、方法和工具，以理解人机物理交互中的神经运动适应机制。人类动力辅助系统，例如，帮助人类操作员操纵重或笨重负载的动力起重装置，需要操作员和机器之间的物理接触，从而形成耦合的动态系统。由于人体刚度的变化，这种耦合动态已被证明会引入固有的不稳定性和性能退化；当遇到不稳定时，操作员通常试图通过加强手臂来控制振荡，这导致系统更僵硬，更不稳定。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机械手之间的接触阻抗，以获得更高的性能和稳定性。本研究将1)理解神经肌肉适应和系统性能限制之间的关联，2)开发概率方法，从生理测量中分类和预测操作员的认知和身体状态的转变，3)将这些知识整合到一个共享的人机结构中，并在车辆装配设施中演示具有现实约束的动力起重设备的有效性。如果成功，该研究将有利于对先进制造和工艺设计的自适应共享控制方法感兴趣的社区，包括汽车，航空航天和军事。这种新一代制造将提高生产效率，缩短组装时间，减少工人的体力负担。研究成果将整合到研究生和本科生的现行课程中。学生将从跨学科和多元文化群体中招募，包括代表性不足的群体。K-12外展将与佐治亚理工学院学生和教师增强伙伴关系计划以及当地非营利协会的夏季机器人营地一起进行。维持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.