Koopman operator regression on physics-simulated cloth data yields a linear surrogate model that enables efficient model predictive control for accurate dynamic folding trajectories on unseen poses in both simulation and real-robot experiments.
Hamiltonian systems and transformation in Hilbert space,
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RC-Koopman uses reservoir computing as a stateful Koopman dictionary with spectral radius controlling temporal memory to achieve accurate and stable identification of nonlinear systems.
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Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
Koopman operator regression on physics-simulated cloth data yields a linear surrogate model that enables efficient model predictive control for accurate dynamic folding trajectories on unseen poses in both simulation and real-robot experiments.
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Koopman Identification of Nonlinear Systems via Reservoir Liftings
RC-Koopman uses reservoir computing as a stateful Koopman dictionary with spectral radius controlling temporal memory to achieve accurate and stable identification of nonlinear systems.
- Limitations of LTI Koopman Modeling for Nonlinear Control Systems