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This project addresses manufacturing tasks that cannot be fully automated because of either the limitations of current algorithms or prohibitive cost and set-up time. Such tasks generally require workers to collaborate in close proximity and adapt to each other's decisions and motions. This project explores accomplishing these tasks through human-robot collaboration. Recent hardware developments in robotics have made human-robot collaboration physically possible, but robots still require new algorithms to ensure safety, efficiency, and fluency when working with people. Creating such algorithms is difficult because there can be high uncertainty in what a person is going to do and how they are going to do it. This project explores the integration of reasoning about how a person moves and how he or she makes decisions into a robot motion planning and decision-making framework. The research centers on the development of new algorithmic frameworks for modeling, simulating, and planning for human-robot collaboration, which requires advances in robot training, task modeling, human motion understanding, high-dimensional motion planning with uncertainty, and metrics to assess human-robot joint action. The results of this project have the potential to signiﬁcantly improve American competitiveness in manufacturing; especially for small-batch manufacturing and burst production, where the cost and set-up time of fully-autonomous solutions is prohibitive. The work will be disseminated in research papers and integrated into curricula. The project is guided by an advisory board from the manufacturing industry, which provides another avenue for dissemination.