Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

TL;DR

This paper introduces a scalable certainty-equivalent EM algorithm for identifying high-dimensional, partially observed nonlinear systems, demonstrated on robotics and helicopter dynamics, outperforming traditional methods.

Contribution

The work formulates certainty-equivalent EM as block coordinate-ascent and provides an efficient implementation for high-dimensional system identification.

Findings

Successfully identified coupled Lorenz attractors in high dimensions.

Achieved better helicopter acceleration prediction than state-of-the-art methods.

Demonstrated scalability and reliability of the approach for complex systems.

Abstract

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.