Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control

Duong, Thai; Altawaitan, Abdullah; Stanley, Jason; Atanasov, Nikolay

doi:10.1109/tro.2024.3428433

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2024

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Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control

Altawaitan,

Stanley,

Ghosal

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control

Altawaitan,

Stanley,

Ghosal

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Extrapolation of Physics-Inspired Deep Networks in Learning Robot Inverse Dynamics

Li,

Wu,

Chen

et al. 2024

Mathematics

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Accurate robot dynamics models are crucial for safe and stable control as well as for generalization to new conditions. Data-driven methods are increasingly used in robotics dynamics modeling for their superior approximation, with extrapolation performance being a critical efficacy indicator. While deep learning is widely used, it often overlooks essential physical principles, leading to weaker extrapolation capabilities. Recent innovations have introduced physics-inspired deep networks that integrate deep learning with physics, leading to improved extrapolation due to their informed structure, but potentially to underfitting in real-world scenarios due to the presence of unmodeled phenomena. This paper presents an experimental framework to assess the extrapolation capabilities of data-driven methods. Using this framework, physics-inspired deep networks are applied to learn the inverse dynamics models of a simulated robotic manipulator and two real physical systems. The results show that under ideal observation conditions physics-inspired models can learn the system’s underlying structure and demonstrate strong extrapolation capabilities, indicating a promising direction in robotics by offering more accurate and interpretable models. However, in real systems their extrapolation often falls short because the physical priors do not capture all dynamic phenomena, indicating room for improvement in practical applications.

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Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control

Cited by 2 publications

References 50 publications

Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control

Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control

Extrapolation of Physics-Inspired Deep Networks in Learning Robot Inverse Dynamics

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