Hugo Buurmeijer

Contacts:

Email: hbuurmei at stanford dot edu

Hugo Buurmeijer

Hugo is a PhD student in the Department of Aeronautics and Astronautics at Stanford University. He completed his BSc degree in Aerospace Engineering at Delft University of Technology in 2022 and his MSc in Aeronautics and Astronautics at Stanford University in 2024. Additionally, he held a research fellow position at Harvard University in the Computational Robotics group directed by professor Heng Yang in 2023. Hugo’s research currently focuses on the control of high-dimensional robotics systems, leveraging both optimal control and novel learning-based architectures, with applications in bio-inspired and soft robotics. He is further interested in autonomy for space missions, and safety analysis of learning-based modules. In his free time, Hugo enjoys watching and playing soccer, reading and traveling.

Awards:

  • Stanford Graduate Fellowship, 2024

ASL Publications

  1. H. Buurmeijer, L. Pabon, J. Alora, R. Kaudinya, G. Haller, and M. Pavone, “Taming High-Dimensional Dynamics: Learning Optimal Projections onto Spectral Submanifolds,” in Proc. IEEE Conf. on Decision and Control, Rio de Janeiro, Brazil, 2025. (In Press)

    [BibTeX] [Abstract]

    Abstract: High-dimensional nonlinear systems pose considerable challenges for modeling and control across many domains, from fluid mechanics to advanced robotics. Such systems are typically approximated with reduced-order models, which often rely on orthogonal projections, a simplification that may lead to large prediction errors. In this work, we derive optimality of fiber-aligned projections onto spectral submanifolds, preserving the nonlinear geometric structure and minimizing long-term prediction error. We propose a data-driven procedure to learn these projections from trajectories and demonstrate its effectiveness through a 180-dimensional robotic system. Our reduced-order models achieve up to fivefold improvement in trajectory tracking accuracy under model predictive control compared to the state of the art.

     
    @inproceedings{BuurmeijerPabonEtAl2025,
  author = {Buurmeijer, H. and Pabon, L. and Alora, J. and Kaudinya, R. and Haller, G. and Pavone, M.},
  title = {Taming High-Dimensional Dynamics: Learning Optimal Projections onto Spectral Submanifolds},
  booktitle = {{Proc. IEEE Conf. on Decision and Control}},
  year = {2025},
  month = dec,
  address = {Rio de Janeiro, Brazil},
  keywords = {press},
  note = {In press},
  owner = {hbuurmei},
  timestamp = {2025-09-18},
  url = {https://arxiv.org/abs/2504.03157}
}