模倣フロー：正規化フローによる深層安定確率動的システムの学習
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
正規化フローを拡張して、大域的に安定な確率的非線形ダイナミクスを模倣学習する手法を提案し、実ロボット実験で有効性を示した論文。
著者: Julen Urain, Michelle Ginesi, Davide Tateo, Jan Peters
分類: cs.LG, cs.RO
原文アブストラクト
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. We show the effectiveness of our method with both standard datasets and a real robot experiment.
