BRITE Fellow: Autonomous Systems that Accommodate Human Perception and Reasoning about Uncertainty

这项促进工程变革和公平进步的研究理念（BRITE）研究员奖将资助在不确定环境中实现自主系统与人类之间可预测的安全互动的研究，并应用于航空航天、制造业、交通运输和医疗保健系统，从而促进科学进步，推进国家繁荣、福利和国防。通过感知环境并对其做出反应，自主系统有可能帮助人类操作员更轻松、更安全地完成困难、危险或有风险的任务。然而，当自主系统显得不可预测、不可靠或反应迟钝时，它们就会成为障碍，而不是帮助的来源。为了防止这种结果，该项目旨在为自主系统开发一种算法设计和控制框架，成功地适应人类行为不可避免的不可预测性，以及系统动态和行动中的不确定性对人类决策的影响。该框架的一个关键要素是它能够捕捉人类的典型需求，从不确定的信息中设计解决方案，并在潜在的冲突约束下，最大程度地满足预期目标。本研究与课程倡议相结合，旨在促进文化响应教学法和基于项目的学习，以培训来自不同背景的学生以人为本的自主系统设计。本研究旨在建立一种基于随机性的方法，将人类对不确定性的感知和推理知识整合到自主动力系统的设计和控制中。为此，新的数学理论和计算算法将基于控制理论、机器学习和人为因素而发展。这样的理论将解决任意，非高斯形式的随机性和理性和非理性决策模型的处理，重点是计算效率的控制器综合。可行性和数学性质的方法，建设性地适应人类的可变性，并具有上下文意识，也将探讨。所开发的算法和理论将在基于仿真的平台上进行实验验证。该项目使首席研究员能够利用在确保随机动力系统概率安全的方法方面的专业知识，以及在自治系统中集成认知模型的持续工作，以追求以人为中心的自治的高风险愿景，具有重大的变革影响潜力。它为推进整个工程社区的文化响应教学和研究实践奠定了基础。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。

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Fellow award will fund research that enables predictably safe interaction between autonomous systems and humans in uncertain environments, with applications in aerospace, manufacturing, transportation, and healthcare systems, thereby promoting the progress of science and advancing the national prosperity, welfare, and defense. By sensing and reacting to their environment, autonomous systems have the potential to help human operators accomplish difficult, dangerous, or risky tasks more easily and safely. However, when autonomous systems appear to be unpredictable, unreliable, or unresponsive, they become hindrances, rather than a source of help. To prevent this outcome, this project aims to develop an algorithmic design and control framework for autonomous systems that successfully accommodates the inevitable unpredictability of human actions, as well as the effects on human decision-making of uncertainty in the dynamics and action of the system. A key element of this framework is its ability to capture typical needs of humans to design solutions from uncertain information, and under potentially conflicting constraints, which meet desired objectives to the greatest possible degree. This research is integrated with curricular initiatives aiming to promote culturally responsive pedagogy and project-based learning in the training of students from diverse backgrounds in human-centric design of autonomous systems.This research aims to make fundamental contributions to a stochasticity-based methodological approach for integrating knowledge of human perception and reasoning about uncertainty into the design and control of autonomous dynamical systems. To this end, new mathematical theory and computational algorithms will be developed, based in control theory, machine learning, and human factors. Such theory will address the handling of arbitrary, non-Gaussian forms of stochasticity and rational and non-rational decision models, with emphasis on computationally efficient controller synthesis. The feasibility and mathematical properties of methods to constructively accommodate human variability, and to be context-aware, will also be explored. The developed algorithms and theories will be experimentally validated in simulation-based platforms. This project enables the principal investigator to leverage expertise in methods for assuring probabilistic safety in stochastic dynamical systems and ongoing work on integrating cognitive models in autonomous systems to pursue a high-risk vision for human-centric autonomy with significant potential for transformational impact. It lays a foundation for advancing culturally responsive teaching and research practices throughout the engineering community.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.