A residual-corrector neural network approximates NMPC optimal moves as state-dependent QPs for input-affine nonlinear systems, trained offline with hybrid imitation and KKT losses and validated on a robotic arm.
Self-supervised learning of iterative solvers for constrained optimization,
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Amortized Nonlinear Model Predictive Control
A residual-corrector neural network approximates NMPC optimal moves as state-dependent QPs for input-affine nonlinear systems, trained offline with hybrid imitation and KKT losses and validated on a robotic arm.