Interface-Aware Intelligence for Robot Teleoperation and Autonomy

用于机器人远程操作和自主的接口感知智能

• 批准号: 2208011
• 负责人: Brenna Argall
• 金额: $ 90万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208011&HistoricalAwards=false
• 关键词: Interface; Aware; Intelligence; Robot; Teleoperation

Humans issue control signals to robot systems in contexts ranging from teleoperation to instruction to shared autonomy, and in domains as wide as space exploration to assistive robotics. Whether via overt body movement or electrical signals from the muscles or brain, for a human to issue a control signal to a robot platform requires physical actuation of an interface. Deviations between the true signal intended by the human and that received from the interface—in magnitude, direction, or timing—can have rippling effects throughout a robotics autonomy system. This award will demonstrate both the need for and utility of interface-aware robotic intelligence. The research will explore how the physical source of the human control signal—their physical capabilities, the interface actuation mechanism, signal transmission limitations, could impose an artificial upper limit on the human-robot team synergy and success. This limitation impacts the teleoperation and autonomy of any robot system, but it can be felt acutely within the domain of assistive robotics, where human motor impairment and accessible interface limitations can result in dramatic operational constraints. As part of the project, annual outreach demos at a local museum will educate K-12 students on assistive robotics. Additionally, undergraduate students will be retained on summer internships for advanced research experience in robotics.The characteristics of a particular interface, operated by a specific human, leave an imprint on the control signal that can be mined for information pertinent to the intelligent interpretation of the human’s control command. In this project, novel robot intelligence paradigms will be designed that aim specifically to complement characteristics of, or compensate for degradations in, control signals issued from a known and characterized combination of control interface and human operator. To do so, a framework for interface-awareness that offers a more complete model of the input pathway from human to robot control system will be designed, and within this framework interface-usage interpretations of and techniques to elicit user-defined maps from human input to robot control space will be developed. Extensive user studies will be performed both to motivate and evaluate the efficacy and impact of interface-aware robotic intelligence within two salient application domains dramatically impacted by the choice of interface activation and mapping: shared autonomy, anchored to physically assistive robots operated by persons with motor impairments, and human-to-robot instruction, anchored to robotic arm behavior demonstration. This work holds the potential to innovate human-machine interactions by mining and modeling information already imprinted upon human-issued control signals, and in doing so achieve a higher level of human-machine symbiosis.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

人类向机器人系统发出控制信号，范围从远程操作到指令到共享自主，以及从空间探索到辅助机器人的广泛领域。无论是通过明显的身体运动还是来自肌肉或大脑的电信号，人类向机器人平台发出控制信号都需要接口的物理致动。人类想要的真实信号和从界面接收到的信号之间的偏差，无论是在幅度、方向还是时间上，都会对整个机器人自治系统产生连锁反应。该奖项将展示界面感知机器人智能的需求和实用性。本研究将探讨人类控制信号的物理来源-他们的身体能力，界面驱动机制，信号传输限制，如何对人类-机器人团队的协同作用和成功施加人为的上限。这种限制影响了任何机器人系统的远程操作和自主性，但在辅助机器人领域中可以敏锐地感受到，人类运动障碍和可访问的界面限制可能导致显着的操作限制。作为该项目的一部分，当地博物馆的年度推广演示将教育K-12学生辅助机器人。此外，本科生将被保留在暑期实习，以获得机器人技术方面的高级研究经验。由特定人类操作的特定界面的特征在控制信号上留下印记，可以挖掘与人类控制命令的智能解释相关的信息。在这个项目中，将设计新的机器人智能范例，专门针对补充的特性，或补偿退化，从一个已知的和特征的控制界面和人类操作员的组合发出的控制信号。要做到这一点，一个框架的接口意识，提供了一个更完整的模型，从人类到机器人控制系统的输入路径将被设计，并在此框架内的接口使用的解释和技术，以引起用户定义的地图从人类输入到机器人控制空间将被开发。将进行广泛的用户研究，以激励和评估界面感知机器人智能在两个显着的应用领域内的有效性和影响，这两个应用领域受到界面激活和映射的选择的极大影响：共享自主权，锚定在由运动障碍人士操作的物理辅助机器人上，以及人对机器人的指令，锚定在机器人手臂行为演示上。这项工作具有通过挖掘和建模已经印在人类发出的控制信号上的信息来创新人机交互的潜力，并在这样做的过程中实现更高水平的人机共生。该项目得到了跨董事会机器人基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF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。