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

• URL: http://arxiv.org/abs/2106.04165v1
• Date: Tue, 8 Jun 2021 08:04:39 GMT
• Title: Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
• Authors: Michael Poli, Stefano Massaroli, Luca Scimeca, Seong Joon Oh, Sanghyuk Chun, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, Animesh Garg
• Abstract summary: We introduce 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 transitions, and end-to-end learning of hierarchical robot controllers.
• Score: 36.81150424798492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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