论文标题
模仿流：通过标准化流量来学习深稳定的随机动态系统
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
论文作者
论文摘要
我们介绍了一种新型的深层生成模型ImitationFlow，它允许学习复杂的全球稳定，随机，非线性动力学。我们的方法扩展了归一化流框架以学习稳定的随机微分方程。我们证明了一类随机微分方程的Lyapunov稳定性，并提出了一种学习算法，以从一组演示的轨迹中学习它们。我们的模型扩展了一组稳定的动力系统，可以通过最新方法来表示，消除了演示中的高斯假设，并以表示准确性来超越先前的算法。我们通过标准数据集和真实的机器人实验显示了我们方法的有效性。
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.
