Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions

by   Michael Poli, et al.

Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes. Stochastic hybrid systems (SHSs), common across engineering domains, provide a formalism for dynamical systems subject to discrete, possibly stochastic, state jumps and multi-modal continuous-time flows. Despite the versatility and importance of SHSs across applications, a general procedure for the explicit learning of both discrete events and multi-mode continuous dynamics remains an open problem. This work introduces Neural Hybrid Automata (NHAs), a recipe for learning SHS dynamics without a priori knowledge on the number of modes and inter-modal transition dynamics. NHAs provide a systematic inference method based on normalizing flows, neural differential equations and self-supervision. We showcase NHAs on several tasks, including mode recovery and flow learning in systems with stochastic transitions, and end-to-end learning of hierarchical robot controllers.



There are no comments yet.


page 8

page 19

page 21


Learning Neural Event Functions for Ordinary Differential Equations

The existing Neural ODE formulation relies on an explicit knowledge of t...

Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems

This paper introduces a novel system identification and tracking method ...

CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis

We propose and evaluate a new technique for learning hybrid automata aut...

Inference of modes for linear stochastic processes

For dynamical systems that can be modelled as asymptotically stable line...

Deep Learning of Conjugate Mappings

Despite many of the most common chaotic dynamical systems being continuo...

Unifying susceptible-infected-recovered processes on networks

Waiting times between two consecutive infection and recovery events in s...

Stability of Planar Switched Systems under Delayed Event Detection

In this paper, we analyse the impact of delayed event detection on the s...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.