Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration - Honda Research Institute USA

Research Intern: Mathematical Foundations of Multi-Agent Task Collaboration

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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

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