Data-Driven Encoding: A New Numerical Method for Computation of the Koopman Operator

Ng, Jerry; Asada, H. Harry

doi:10.1109/lra.2023.3273515

IEEE Robot. Autom. Lett.

2023

DOI: 10.1109/lra.2023.3273515

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Data-Driven Encoding: A New Numerical Method for Computation of the Koopman Operator

Jerry Ng

H. Harry Asada

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2024

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Cited by 2 publications

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A Monte Carlo Approach to Koopman Direct Encoding and Its Application to the Learning of Neural-Network Observables

Nozawa,

Kamienski,

O'Neill

et al. 2024

IEEE Robot. Autom. Lett.

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A Monte Carlo Approach to Koopman Direct Encoding and Its Application to the Learning of Neural-Network Observables

Nozawa,

Kamienski,

O'Neill

et al. 2024

IEEE Robot. Autom. Lett.

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A Koopman-based residual modeling approach for the control of a soft robot arm

Bruder,

Bombara,

Wood

2024

The International Journal of Robotics Research

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Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

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Data-Driven Encoding: A New Numerical Method for Computation of the Koopman Operator

Cited by 2 publications

References 21 publications

A Monte Carlo Approach to Koopman Direct Encoding and Its Application to the Learning of Neural-Network Observables

A Monte Carlo Approach to Koopman Direct Encoding and Its Application to the Learning of Neural-Network Observables

A Koopman-based residual modeling approach for the control of a soft robot arm

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