Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic TransitionsDownload PDF

21 May 2021, 20:46 (modified: 25 Jan 2022, 00:11)NeurIPS 2021 PosterReaders: Everyone
Keywords: dynamical systems, time series, neural differential equations, control, stochastic hybrid systems
TL;DR: We introduce Neural Hybrid Automata (NHAs), a procedure designed to enable learning and simulation of stochastic hybrid systems from data.
Abstract: Effective control and prediction of dynamical systems 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, mode parameters, 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.
Supplementary Material: pdf
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
9 Replies