CAREER: Making Robots More Cooperative Agents: Controlling Costs of Coordination Through Graph-Based Models of Joint Activity

职业：让机器人更具合作性：通过基于图的联合活动模型控制协调成本

• 批准号: 2238402
• 负责人: Martijn Ijtsma
• 金额: $ 55.46万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238402&HistoricalAwards=false
• 关键词: CAREER; Making; Robots; More; Cooperative

The deployment of smart robots promises increased safety, productivity, and capability in domains such as disaster and emergency response, ground mobility, manufacturing, aviation, and space operations. Good human-robot collaboration is key to realizing these promises. This project develops novel modeling techniques for analyzing and designing collaborative behavior in human-robot teams. Collaborative behavior requires adjusting to and communicating with each other. Coordination and communication incur cognitive and temporal costs. In human-robot collaboration these costs can be high, as coordination with autonomous agents generally is more taxing and time-consuming than collaboration with other humans. The models developed in this project will identify the causes and effects of coordination costs in human-robot systems. Based on these models, the project develops techniques for managing coordination costs to avoid overloading human operators. Improved cost management will lead to more robust and resilient human-robot operations, broader adoption of smart robotic technologies, and realization of their promise. The project integrates the research with education and outreach activities to train the future workforce in systems thinking and interdisciplinary problem-solving skills. These skills will ready future engineers, researchers, and scientists to create integrated solutions to address complex challenges that span technological, human, ecological, economic, and policy dimensions.This project develops a generalizable formalization for representing and analyzing joint activity in human-robot systems by combining theories from cognitive and social sciences with techniques from graph theory and agent-based modeling. This framework allows objective and dynamic analysis of the teamwork required to manage interdependencies between humans and robots. Based on the model, the research develops techniques for dynamically adapting and controlling coordination costs to improve collaboration and avoid lapses. The work will be validated in disaster response and space operations. The project addresses three fundamental research challenges: First, it determines the relation between a human-robot system organization, asymmetries in cooperative competencies, and cognitive and temporal costs of coordinating with robots. Second, it identifies control strategies for dynamically regulating coordination costs in human-robot systems. Third, it demonstrates the use of graph-theoretical metrics and algorithms to translate theoretical concepts of joint activity into actionable guidance for making robots more cooperative agents in dynamic environments. Findings will provide deep insight into what capabilities robots need to be endowed with to make them useful cooperative agents in context. These insights will tell us how robotic functionality should be deployed to improve the robustness and resilience of complex operations.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.

智能机器人的部署有望提高灾难和应急响应、地面移动、制造、航空和太空作业等领域的安全性、生产力和能力。良好的人机协作是实现这些承诺的关键。该项目开发了新颖的建模技术，用于分析和设计人机团队的协作行为。协作行为需要相互适应和沟通。 协调和沟通会产生认知和时间成本。在人机协作中，这些成本可能很高，因为与自主代理的协调通常比与其他人的协作更加费力和耗时。该项目开发的模型将确定人机系统中协调成本的原因和影响。基于这些模型，该项目开发了管理协调成本的技术，以避免操作人员超载。改进的成本管理将带来更强大、更有弹性的人机操作、更广泛地采用智能机器人技术并实现其承诺。该项目将研究与教育和外展活动结合起来，培训未来劳动力的系统思维和跨学科问题解决技能。这些技能将使未来的工程师、研究人员和科学家做好准备，创建综合解决方案，以应对跨越技术、人类、生态、经济和政策维度的复杂挑战。该项目通过将认知和社会科学的理论与图论和基于代理的建模技​​术相结合，开发了一种通用的形式化来表示和分析人机系统中的联合活动。该框架允许对管理人类和机器人之间相互依赖关系所需的团队合作进行客观和动态的分析。基于该模型，该研究开发了动态调整和控制协调成本的技术，以改善协作并避免失误。这项工作将在灾难响应和太空行动中得到验证。该项目解决了三个基本研究挑战：首先，它确定了人机系统组织、合作能力的不对称性以及与机器人协调的认知和时间成本之间的关系。其次，它确定了动态调节人机系统协调成本的控制策略。第三，它演示了如何使用图论度量和算法将联合活动的理论概念转化为可操作的指导，使机器人在动态环境中更具合作性。研究结果将深入了解机器人需要具备哪些能力才能使其成为在环境中有用的合作代理。 这些见解将告诉我们如何部署机器人功能，以提高复杂操作的稳健性和弹性。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。