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arXiv · 1804.04878

Learning Contracting Vector Fields For Stable Imitation Learning

Vikas Sindhwani·Stephen Tu·Mohi Khansari
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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.

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Vikas Sindhwani, Stephen Tu, Mohi Khansari. 2018-04-13. Learning Contracting Vector Fields For Stable Imitation Learning. https://arxiv.org/abs/1804.04878

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