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Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Kunal Menda · Jean de Becdelievre · Jayesh K. Gupta · Ilan Kroo · Mykel Kochenderfer · Zachary Manchester

Keywords: [ Non-convex Optimization ] [ Robotics ] [ Time Series and Sequence Models ] [ Unsupervised Learning ] [ Sequential, Network, and Time-Series Modeling ]


System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear systems. We empirically show that the certainty-equivalent approximation to expectation-maximization can be a reliable and scalable approach for high-dimensional deterministic systems, which are common in robotics. We formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation. The algorithm is tested on a simulated system of coupled Lorenz attractors, demonstrating its ability to identify high-dimensional systems that can be intractable for particle-based approaches. Our approach is also used to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, a model is learned that predicts the acceleration of the helicopter better than state-of-the-art approaches. The codebase for this work is available at

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