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. P. B. Eberhard, L. Pabon, D. Gammelli, H. Buurmeijer, A. Lahr, M. Leone, A. Carron, and M. Pavone, “Graph Neural Model Predictive Control for High-Dimensional Systems,” in Proc. IEEE Conf. on Robotics and Automation, 2026.

    [BibTeX] [Abstract]

    Abstract: The control of high-dimensional systems, such as soft robots, requires models that faithfully capture complex dynamics while remaining computationally tractable. This work presents a framework that integrates Graph Neural Network (GNN)-based dynamics models with structure-exploiting Model Predictive Control to enable real-time control of high-dimensional systems. By representing the system as a graph with localized interactions, the GNN preserves sparsity, while a tailored condensing algorithm eliminates state variables from the control problem, ensuring efficient computation. The complexity of our condensing algorithm scales linearly with the number of system nodes, and leverages Graphics Processing Unit (GPU) parallelization to achieve real-time performance. The proposed approach is validated in simulation and experimentally on a physical soft robotic trunk. Results show that our method scales to systems with up to 1,000 nodes at 100 Hz in closed-loop, and demonstrates real-time reference tracking on hardware with sub-centimeter accuracy, outperforming baselines by 63.6%. Finally, we show the capability of our method to achieve effective full-body obstacle avoidance.

     
    @inproceedings{EberhardPabonGammelliEtAl2026,
  author = {Eberhard, P. B. and Pabon, L. and Gammelli, D. and Buurmeijer, H. and Lahr, A. and Leone, M. and Carron, A. and Pavone, M.},
  title = {Graph Neural Model Predictive Control for High-Dimensional Systems},
  booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
  year = {2026},
  keywords = {pub},
  owner = {gammelli},
  timestamp = {2026-3-4},
  url = {https://arxiv.org/abs/2602.17601}
}
  2. 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}
}