Wang studying learning coordination for Multi-Autonomous Multi-Human (MAMH) agent systems with guaranteed safety

Xuan Wang, Assistant Professor, Electrical and Computer Engineering, is designing a framework for Multi-Autonomous Multi-Human (MAMH) systems. 

The operation of many real-world systems involves the co-existence of human and autonomous agents. Inadequate coordination among these agents can lead to significant performance degradation or safety risks.  

In this project, Wang aims to develop a novel framework for Multi-Autonomous Multi-Human coordination, which could enhance algorithmic scalability and safety guarantees.  

Compared with traditional optimization and machine learning approaches, the proposed framework addresses two major challenges: (i) the non-cooperative nature of the system, which arises from information asymmetry between humans and robots, heterogeneity in human preferences, and human selfishness in decision-making when working with robots; and (ii) coordination safety, which is of critical importance in the presence of human agents but is difficult to measure using traditional black-box learning models. Additionally, human behaviors are subject to uncertainties, which might easily deviate the actual coordination from intended ones. 

Wang has designed this project to have special emphasis on workforce development and education. He has incorporated a carefully designed "RoboArt" event that will engage K-12 students and foster creativity, problem-solving skills, and STEM exposure.  

The project will also offer multidisciplinary learning and research opportunities for high school and university students, ensuring inclusive access to the evolving field of robotics and machine learning. Furthermore, the project will contribute valuable datasets to the research community, emphasizing accessibility and re-usability to facilitate ongoing innovation in the field. 

Wang received $344,626 from the National Science Foundation for this project. Funding began in Jan. 2024 and will end in late Dec. 2026. 

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