EAGER: Shared Visual Common Ground in Human-Robot Interaction for Small Unmanned Aerial Systems

该项目将创建一个视觉共同点的计算理论，允许用户向机器人（或其他团队成员）发出指令，并通过共享的视觉显示器进行视觉通信来接收确认或约束。激励的例子是一个城市搜索和救援（US R）专业人员在iPad上点击，绘制草图和注释，以便在未经培训的情况下指挥小型无人机系统（sUAS）。以前在人机交互方面的工作仅限于自然语言，但最近的工作表明，让所有团队成员看到无人地面机器人的机器人眼睛视图显着提高了性能和态势感知能力。 拟议的工作填充计算理论使用共享角色模型来表示输入（指令，符号），输出（显示视点，形式，大小，位置，内容等）， 视觉通信引擎（Visual Communication Engine） 计算理论将原型化，完善，并由美国R从业者在得克萨斯州A M的灾难城飞行现实的sUAS任务进行测试。智力价值：该项目将创建一个计算理论的视觉共同点，将使双向人机交互使用视觉通信机制，如点击，素描，并在共享的视觉显示器上的注释移动的设备，如iPad，智能手机和平板电脑。研究结果将推动人机交互、人工智能和认知科学领域的发展。更广泛的影响：这一结果可能会彻底改变人们使用移动的设备与机器人（以及彼此）互动的方式，这些设备使用自然的视觉机制，而不必经过大量的训练。该项目将通过REU方案积极招募妇女、西班牙裔和残疾人参加。将为HRI研究人员制作一个开放源码的视觉通信工具包。研究结果将改善公共安全、远程医疗和远程办公的机器人，并通过纳入德克萨斯州第一特遣部队，立即帮助拯救生命。

This project will create a computational theory of visual common ground, allowing users to give directives to a robot (or other team members) and receive confirmation or constraints through visual communication over a shared visual display. The motivating example is an urban search and rescue (US&R) professional tapping, sketching, and annotating on an iPad in order to direct a small unmanned aerial system (sUAS) without training. Previous work in human-robot interaction with common ground has been limited to natural language, but recent work has shown that having all team members see the robot's eye view in unmanned ground robots significantly improved performance and situation awareness. The proposed work populate the computational theory using the Shared Roles Model to represent the inputs (directives, notations), outputs (display viewpoint, form, size, location, content, etc.), and transformations (visual communication engine). The computational theory will be prototyped, refined, and tested by US&R practitioners flying realistic sUAS missions at Texas A&M's Disaster City.Intellectual merit: The project will create a computational theory of visual common ground that will enable two-way human-robot interaction using visual communication mechanisms such as tapping, sketching, and annotation on shared visual displays on mobile devices such as iPads, smartphones, and tablet PCs. The results will advance the fields of human-robot interaction, artificial intelligence, and cognitive science. Broader impacts: The results could revolutionize how people use mobile devices to interact with robots (and with each other) using naturalistic visual mechanisms, bypassing extensive training. The project will actively recruit women, Hispanics, and persons with disabilities to participate through REU programs. An open source visual communication toolkit for HRI researchers will be produced. The results will improve robots for public safety, remote medicine, and telecommuting, and could also immediately help save lives through incorporation into Texas Task Force 1.