Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration

Job Number: P25INT-61 Honda Research Institute USA (HRI-US) is seeking a Honda Research Institute USA (HRI-US) is seeking a research intern to investigate the mathematical foundations of human-robot collaboration. The intern will develop methods for quantifying when and how much multi-agent systems can benefit from coordination, with a particular focus on optimal transport and distribution-based models of agent behavior. The work will combine mathematical analysis, algorithm development, and computational experiments to study fundamental properties of coordination in human-robot and multi-agent systems. San Jose, CA **Key Responsibilities** * Develop and analyze mathematical models for quantifying the benefit of coordination in multi-agent systems. * Apply optimal transport and related optimization methods to joint distributions over agent behaviors. * Investigate how coordination benefit depends on task structure, agent behavior models, and interaction geometry. * Develop theoretical results and computational experiments characterizing properties of coordination metrics. * Implement and evaluate algorithms in Python using numerical optimization and scientific computing tools. * Design simulation experiments to validate theoretical predictions and explore extensions to human-robot and multi-agent settings. * Contribute to research publications and presentations resulting from the work. **Minimum Qualifications** * Currently pursuing a Ph.D. in Computer Science, Robotics, Applied Mathematics, Electrical Engineering, Statistics, Operations Research, or a closely related field. * Strong mathematical background, including probability, linear algebra, and optimization. * Experience formulating and analyzing mathematical or computational models. * Proficiency in Python and scientific computing. * Ability to independently develop, implement, and evaluate research ideas. **Bonus Qualifications** * Experience with optimal transport, convex optimization, or distributional optimization. * Background in probability theory, information theory, statistical learning, or mathematical statistics. * Experience with multi-agent systems, robotics, human-robot interaction, or decision-making. * Familiarity with numerical optimization packages such as CVXPY, SciPy, or related tools. * Experience developing theoretical results alongside computational experiments. * Strong research publication record in machine learning, robotics, optimization, applied mathematics, or related areas. **Years of Work Experience Required** 0 **Desired Start Date** 1/11/2027 **Internship Duration** 3 Months **Position Keywords** Optimal transport; human-robot collaboration; multi-agent systems; coordination; coordination benefit; probability distributions; convex optimization; distributional optimization; information theory; mathematical modeling; decision-making; robotics; machine learning; statistical dependence **Warning:**You do not have the permission to access the upload fields on this form. Contact the form owner or portal administrator to request the access.