Neural Control introduces adjoint-based differentiation through implicit equilibrium constraints to enable memory-efficient gradient computation and robust receding-horizon MPC for multi-stable deformable object manipulation, outperforming gradient-free baselines in simulation and hardware.
arXiv preprint arXiv:2010.07078 , year=
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Neural Control: Adjoint Learning Through Equilibrium Constraints
Neural Control introduces adjoint-based differentiation through implicit equilibrium constraints to enable memory-efficient gradient computation and robust receding-horizon MPC for multi-stable deformable object manipulation, outperforming gradient-free baselines in simulation and hardware.