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Learning Contracting Vector Fields For Stable Imitation Learning
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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.
Forward citations
Cited by 3 Pith papers
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G2-Nav: Grounded and Guarded Vision-Language Costmaps for Robot Social Navigation
G2-Nav turns vision-language reasoning about social scenes into a weighted costmap with a safety reflex layer, tested in recorded and live real-world trials.
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Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
A contractive dynamical-system policy, built from recurrent equilibrium networks and coupling layers, that guarantees out-of-sample recovery and is shown to beat stable baselines on imitation benchmarks.
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Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
An extended NCDS framework learns multiple robot skills from a single network by conditioning on task variables and performs obstacle avoidance in the latent space while preserving contraction-based stability.
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