  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 https://github.com/sisl/CEEM.
