Context-Aware Deep Lagrangian Networks for Model Predictive Control

Lucas Schulze1, Jan Peters1, Oleg Arenz1
1Intelligent Autonomous Systems Lab, TU Darmstadt

Accepted to the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)

Code Paper arXiv

Overview

The following highlights the main contributions of our work:

  • Combine Deep Lagrangian Networks (DeLaN) with a nominal model to predict only the unknown dynamics.
  • Extend DeLaN to a contextual setting with a history encoder, enabling a single network to adapt to different dynamics and perform online identification of physically consistent models.
  • Integrate the Context-Aware DeLaN into an MPC — CaDeLaC — built using software frameworks (e.g., PyTorch, Acados, Pinocchio, L4CasADi).
  • Demonstrate the performance of CaDeLaC in zero-shot experiments on a real 7-DOF Franka Emika Panda robot, using a model trained fully in simulation.

High-Speed Trajectories

CaDeLaC tracks high-speed trajectories near joint velocity limits with different payloads (1 kg, 2 kg, 3 kg), outperforming pure gravity compensation methods.

Pick-and-Place

Our method performs a pick-and-place task with 3 kg and 2 kg payloads, adapting online to changing dynamics in the environment.

BibTeX

If you find this work useful, please consider citing:

@inproceedings{schulze2025contextawaredelan,
  author={Schulze, Lucas and Peters, Jan and Arenz, Oleg},
  booktitle={2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Context-Aware Deep Lagrangian Networks for Model Predictive Control}, 
  year={2025},
  volume={},
  number={},
  pages={6939-6946},
  keywords={},
  doi={10.1109/IROS60139.2025.11246292}
}

Acknowledgment

This project has been funded by the German Federal Ministry of Research, Technology and Space (BMFTR) - Project number 01IS23057B.