Investigation of User-Interface and Human-Robot Performance for Supernumerary Robots

辅助机器人是一种可以附着在人体上的辅助装置，可以控制它与人的自然肢体进行协调动作。在建筑、农业应用、搜救和外太空探索等体力要求高和/或危险的工作场所活动中，多余的机器人有可能降低风险，提高人类的能力。该项目的研究目标是在“第三臂”多余机器人的实时灵巧控制背景下，推进对人机交互的基本理解。该项目将开发一种新的人机界面，允许人类使用肌电图活动（EMG）以闭环方式使用来自视觉和补充振动触觉信息的多模态感官反馈来指挥机器人。项目团队将在两组人体实验中使用新系统，旨在探索在人类学习在一种新的三手操作任务中控制机器时发生的双向适应，即使机器人控制器随着人类用户疲劳而适应肌肉肌电信号变化的信号特性。该项目推进了美国国家科学基金会的使命，即通过推进对人机协同适应伙伴关系的基本理解，促进科学进步，促进国家健康、繁荣和福利。该项目的更广泛影响包括为中小女生建立一个暑期课程，介绍机器人、传感器、神经科学和人类表现的原理。这个暑期项目与美国国家科学基金会的目标直接一致，即支持旨在增加妇女和其他未被充分代表的群体参与科学和技术的活动。本项目探讨了“第三条手臂”辅助机器人背景下的人机合作。计划进行两组人体实验。首先，建立了一种新颖的、自适应的、基于肌电图的方法，用于触觉引导闭环控制多余机械臂末端执行器的可行性。将记录几种对认知状态敏感的生物信号，包括瞳孔测量、皮肤电反应、心率变异性和脑电图（EEG）。受试者将执行点对点轨迹任务和Fitts定律任务的3D版本，以评估有和没有应用于用户脚的振动触觉性能反馈的控制效率。第二部分探讨了短期人类学习三手操作任务的耦合动力学，该任务要求用户将双手的运动与多余的机器人的运动协调起来，以操纵一个新的三元素物体来重现特定的期望姿势。同时，机器人握力的在线计算学习适应将由使用者的自然手臂引起的物体运动学和动力学扰动驱动。这项工作将促进对以下方面的基本理解：(1)物理工作空间中的人机界面性能，其中认知体现在人类和多余的机器人中；(2)脑电及其他生理测量结果显示认知负荷与神经可塑性；(3)短期训练提高了三手协调和操作能力。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。

Supernumerary robots are assistive devices that may attach to the human body and be controlled to perform coordinated actions with a person's natural limbs. Supernumerary robots have potential to reduce risk and increase human capability in physically-demanding and/or dangerous workplace activities, including construction, agricultural applications, search-and-rescue, and outer space exploration. The research objective of this project is to advance a fundamental understanding of human-machine interaction within the context of real-time dexterous control of a "third arm" supernumerary robot. The project will develop a novel human-machine interface that will allow a human to use electromyographic activity (EMG) to command the robot in a closed-loop manner using multimodal sensory feedback derived from visual and supplemental vibrotactile information. The project team will use the new system in two sets of human subject experiments designed to explore the bi-directional adaptation that occurs within the human-robot dyad as the human learns to control the machine in a novel three-handed manipulation task, even as the robotic controller adapts to the changing signal properties of muscle EMGs as the human user fatigues. The project advances the National Science Foundation's mission to promote the progress of science, to advance national health, prosperity, and welfare, by advancing a fundamental understanding of co-adaptive human-machine partnerships. Broader impacts of this project include establishing a summer program for elementary and middle school female students to introduce principles of robotics, sensors, neuroscience, and human performance. The summer program is directly aligned with the National Science Foundation's goal to support activities designed to increase the participation of women and other underrepresented groups in science and technology.This project explores human-machine cooperation within the context of a "third arm" supernumerary robot. Two sets of human subject experiments are planned. The first establishes the feasibility of a novel, adaptive, EMG-based methodology for haptic-guided closed-loop control of a supernumerary robotic arm's end-effector. Several biosignals sensitive to cognitive state will be recorded, including pupillometry, galvanic skin response, heart rate variability, and electroencephalography (EEG). Subjects will perform a point-to-point trajectory task and a 3D version of the Fitts's Law task to assess control efficiency with and without vibrotactile performance feedback applied to the user's foot. The second explores the coupled dynamics of short-term human learning of a three-handed manipulation task that requires the user to coordinate motions of their two hands with those of the supernumerary robot to manipulate a novel three-element object to recreate specific desired poses. At the same time, online computational learning adaptations of the robot's grip force will be driven by perturbations of object kinematics and kinetics caused by the user's natural arms. The work will advance a fundamental understanding of (1) human interface performance in physical workspaces where cognition is embodied in both the human and supernumerary robot; (2) cognitive load and neuroplasticity using EEG and other physiological measurements; and (3) improvements in tri-manual coordination and manipulation capability arising from short-term training.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.