Related papers: Learning Stable Deep Dynamics Models

Learning Stable Deep Dynamics Models

URL: http://arxiv.org/abs/2001.06116v1
Date: Fri, 17 Jan 2020 00:04:45 GMT
Title: Learning Stable Deep Dynamics Models
Authors: Gaurav Manek, J. Zico Kolter
Abstract summary: We propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space. We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics.
Score: 91.90131512825504
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs). Despite the predictive power of these systems, it has been difficult to make formal claims about the basic properties of the learned systems. In this paper, we propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space. The approach works by jointly learning a dynamics model and Lyapunov function that guarantees non-expansiveness of the dynamics under the learned Lyapunov function. We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics, such as video textures, in a fully end-to-end fashion.

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