ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows

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We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics.

• Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations.
• We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to learn them from a set of demonstrated trajectories.
• Our model extends the set of stable dynamical systems that can be represented by state-of-the-art approaches, eliminates the Gaussian assumption on the demonstrations, and outperforms the previous algorithms in terms of representation accuracy.