A divide-and-conquer method learns stable linear parameter varying dynamical systems in high-dimensional robot joint space by optimizing subsystems and compositing them with a joint Lyapunov function.
An approach for imitation learning on Riemannian manifolds,
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Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
A divide-and-conquer method learns stable linear parameter varying dynamical systems in high-dimensional robot joint space by optimizing subsystems and compositing them with a joint Lyapunov function.