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Learning deep dynamical models from image pixels

Wahlström, N; Schön, TB; Deisenroth, MP; (2015) Learning deep dynamical models from image pixels. IFAC-PapersOnLine , 48 (28) pp. 1059-1064. 10.1016/j.ifacol.2015.12.271. Green open access

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Modeling dynamical systems is important in many disciplines, such as control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement mapping and the transition mapping (system dynamics) in latent space can be challenging. For linear system dynamics and measurement mappings efficient solutions for system identification are available. However, in practical applications, the linearity assumptions does not hold, requiring nonlinear system identification techniques. If additionally the observations are high-dimensional (e.g., images), nonlinear system identification is inherently hard. To address the problem of nonlinear system identification from high-dimensional observations, we combine recent advances in deep learning and system identification. In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space. We demonstrate that our model enables learning good predictive models of dynamical systems from pixel information only.

Type: Article
Title: Learning deep dynamical models from image pixels
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ifacol.2015.12.271
Publisher version: https://doi.org/10.1016/j.ifacol.2015.12.271
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep neural networks, system identification, nonlinear systems, low-dimensional embedding, auto-encoder
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10083571
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