Authors

Thai Duong, Abdullah Altawaitan, Jason Stanley, Nikolay Atanasov

Abstract

Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently accurate, even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots are described in terms of their generalized coordinates on a matrix Lie group, e.g., on \( ext{SE}(3) \) for ground, aerial, and underwater vehicles, and generalized velocity, and satisfy conservation of energy principles. This article proposes a port-Hamiltonian formulation over a Lie group of the structure of a neural ordinary differential equation (ODE) network to approximate the robot dynamics. In contrast to a black-box ODE network, our formulation embeds energy conservation principle and Lie group’s constraints in the dynamics model and explicitly accounts for energy-dissipation effect such as friction and drag forces in the dynamics model. We develop energy shaping and damping injection control for the learned, potentially under-actuated Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various robot platforms.

Citation

  • Journal: IEEE Transactions on Robotics
  • Year: 2024
  • Volume: 40
  • Issue:
  • Pages: 3695–3715
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • DOI: 10.1109/tro.2024.3428433

BibTeX

@article{Duong_2024,
  title={{Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control}},
  volume={40},
  ISSN={1941-0468},
  DOI={10.1109/tro.2024.3428433},
  journal={IEEE Transactions on Robotics},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Duong, Thai and Altawaitan, Abdullah and Stanley, Jason and Atanasov, Nikolay},
  year={2024},
  pages={3695--3715}
}

Download the bib file

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