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- W4301263213 abstract "We propose a new non-parametric framework for learning incrementally stable dynamical systems x' = f(x) from a set of sampled trajectories. We construct a rich family of smooth vector fields induced by certain classes of matrix-valued kernels, whose equilibria are placed exactly at a desired set of locations and whose local contraction and curvature properties at various points can be explicitly controlled using convex optimization. With curl-free kernels, our framework may also be viewed as a mechanism to learn potential fields and gradient flows. We develop large-scale techniques using randomized kernel approximations in this context. We demonstrate our approach, called contracting vector fields (CVF), on imitation learning tasks involving complex point-to-point human handwriting motions." @default.
- W4301263213 created "2022-10-05" @default.
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- W4301263213 date "2018-04-13" @default.
- W4301263213 modified "2023-09-25" @default.
- W4301263213 title "Learning Contracting Vector Fields For Stable Imitation Learning" @default.
- W4301263213 doi "https://doi.org/10.48550/arxiv.1804.04878" @default.
- W4301263213 hasPublicationYear "2018" @default.
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