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NSF2026: EAGER:Cues and actions for efficient nonverbal human-robot communication

NSF2026：EAGER：高效非语言人机交流的提示和动作

基本信息

批准号：
2033918
负责人：
Sachit Butail
金额：
$ 13.87万
依托单位：
Northern Illinois University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033918&HistoricalAwards=false
关键词：
NSF2026 EAGER Cues actions efficient

项目摘要

A large part of human group communication takes place nonverbally. People follow gaze, avoid collisions, search and rescue in teams, all without speaking to each other. In this respect, actions, rather than words, enable rapid two-way communication of information without interfering with the task at hand or posing additional mental burden required to understand speech and text. Integrating human-machine intelligence would benefit from a similar natural and fluid communication between humans and machines. This project develops novel methods to advance human-robot intelligence through a series of experimental studies and rigorous mathematical analysis. The experiments involve tasks designed to exploit the strengths of robots and humans; robots are able to repetitively explore a large environment and humans have better awareness of the situation and domain expertise. The experimental tasks are inspired by the difficult problem of monitoring the vast number of invasive aquatic species threatening the Great Lakes region. The mathematical analysis is aimed at discovering effective robot actions in response to changes in human cognitive load, and efficient nonverbal interaction strategies between humans and robots. Research results from this work will raise public awareness of invasive aquatic species in the Great Lakes region and present human-robot teaming as a prominent opportunity to solve large-scale problems. Engineering students involved in the project will contribute to the new generation of scientific workforce who can straddle boundaries across multiple disciplines such as robotics, computer science, and ecology.This research aims to enable tighter integration of human-robot intelligence by teasing out components of efficient nonverbal human-robot communication. These include: (i) level of engagement of the robotic swarm as a function of human cognitive load, (ii) recruitment strategies used by humans as they team up with the robotic swarm to map a complex dynamic environment, (iii) perception of robot swarming patterns by humans, and (iv) indirect indicators of human cognitive load that can enable faster interpretation in the wild. Towards this, experimental conditions will highlight the dependence of team performance on how robots respond to the cognitive load experienced by the human participants. Experiments will be conducted in virtual reality to enable realization of large robot swarms without the accompanying design and sensor programming challenges. Scalability of swarm robotics will be preserved by building all environmental sensing and interaction strategies upon local interaction rules. Information-theoretic measures of directional information flow will be used to quantify human perception of swarm patterns and isolate movement correlates of cognitive load. This project has the support of the Human-Centered-Computing Program in the IIS Division in the CISE Directorate, and the NSF 2026 Fund Program in the Office of Integrated Activities. The project enriches, extends, and explores the NSF 2026 Idea Machine Winning Entry “Integrated Human-Machine Intelligence”.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.

大部分的人际交流都是通过非语言的方式进行的。人们跟随目光，避免碰撞，团队搜索和救援，所有这些都没有互相说话。在这方面，行动，而不是言语，能够实现快速的双向信息交流，而不会干扰手头的任务，也不会造成理解语音和文本所需的额外心理负担。整合人机智能将受益于人类和机器之间类似的自然和流畅的通信。该项目通过一系列实验研究和严格的数学分析，开发新的方法来提高人机智能。这些实验涉及旨在利用机器人和人类优势的任务;机器人能够重复探索大环境，而人类对情况和领域专业知识有更好的认识。实验任务的灵感来自于监测威胁五大湖地区的大量入侵水生物种的难题。数学分析的目的是发现有效的机器人行动，以应对人类认知负荷的变化，以及人类和机器人之间的有效的非语言交互策略。这项工作的研究成果将提高公众对五大湖地区入侵水生物种的认识，并将人机合作作为解决大规模问题的重要机会。参与该项目的工程专业学生将为新一代的科学工作者做出贡献，他们可以跨越机器人技术、计算机科学和生态学等多个学科的界限。该研究旨在通过梳理高效的非语言人机沟通组件，实现人机智能的更紧密集成。其中包括：（i）作为人类认知负荷的函数的机器人群体的参与水平，（ii）当人类与机器人群体合作以绘制复杂的动态环境时人类使用的招募策略，（iii）人类对机器人群集模式的感知，以及（iv）可以在野外实现更快解释的人类认知负荷的间接指标。为此，实验条件将突出团队绩效对机器人如何响应人类参与者所经历的认知负荷的依赖性。实验将在虚拟现实中进行，以实现大型机器人群，而无需伴随设计和传感器编程的挑战。群体机器人的可扩展性将通过建立所有的环境感知和互动策略本地互动规则。定向信息流的信息理论测量将被用来量化人类对群体模式的感知，并隔离认知负荷的运动相关性。该项目得到了CISE理事会IIS部门的以人为中心的计算计划和综合活动办公室的NSF 2026基金计划的支持。该项目丰富、扩展和探索了NSF 2026 Idea Machine获奖作品“集成人机智能”。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。