Neural Contractive Dynamical Systems

Hadi Beik-Mohammadi, Søren Hauberg, Georgios Arvanitidis, Nadia Figueroa, Gerhard Neumann, Leonel Rozo

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Abstract

Stability guarantees are crucial when ensuring a fully autonomous robot does not take undesirable or potentially harmful actions. Unfortunately, global stability guarantees are hard to provide in dynamical systems learned from data, especially when the learned dynamics are governed by neural networks. We propose a novel methodology to learn neural contractive dynamical systems, where our neural architecture ensures contraction, and hence, global stability.To efficiently scale the method to high-dimensional dynamical systems, we develop a variant of the variational autoencoder that learns dynamics in a low-dimensional latent representation space while retaining contractive stability after decoding. We further extend our approach to learning contractive systems on the Lie group of rotations to account for full-poseend-effector dynamic motions. The result is the first highly flexible learning architecture that provides contractive stability guarantees with capability to perform obstacle avoidance. Empirically, we demonstrate that our approach encodes the desired dynamics more accurately than the current state-of-the-art,which provides less strong stability guarantees.
Original languageEnglish
Title of host publicationProceedings of The Twelfth International Conference on Learning Representations (ICLR 2024)
Number of pages24
Publication date2024
Publication statusPublished - 2024
EventThe Twelfth International Conference on Learning Representations - Vienna, Austria
Duration: 7 May 202411 May 2024
Conference number: 12

Conference

ConferenceThe Twelfth International Conference on Learning Representations
Number12
Country/TerritoryAustria
CityVienna
Period07/05/202411/05/2024

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