Learning Coordination for Multi-Autonomous Multi-Human (MAMH) Agent Systems with Guaranteed Safety

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. This project aims to develop a novel framework for Multi-Autonomous Multi-Human coordination, which enhances 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 may easily deviate the actual coordination from intended ones. To address these challenges, the intellectual merits of this research lie in its innovative integration of game theory, machine learning, human modeling, and network control theory, resulting in a framework for Multi-Autonomous Multi-Human coordination that enhances both model transparency and learnability. Core to the framework is a novel human-response alignment mechanism, allowing autonomous agents in the system to not only passively adapt to human behaviors but also subtly guide them, enhancing the efficiency and safety of the entire system. To facilitate this, computationally scalable and efficient algorithms will be developed in the manner of distributed-training-distributed execution, purely based on agents’ local resources for communication and computation. The broader impacts of this work extend to various engineering practices, including traffic coordination, human-robot teaming, and power/IoT systems involving human users. The project has a special emphasis on workforce development and education. A carefully designed "RoboArt" event will engage K-12 students, fostering 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. 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.