Timezone: »

 
Poster
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda · Jean de Becdelievre · Jayesh K. Gupta · Ilan Kroo · Mykel Kochenderfer · Zachary Manchester

Tue Jul 14 08:00 AM -- 08:45 AM & Tue Jul 14 09:00 PM -- 09:45 PM (PDT) @ None #None

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.

Author Information

Kunal Menda (Stanford University)
Jean de Becdelievre (Stanford University)
Jayesh K. Gupta (Stanford University)
Ilan Kroo (Stanford University)
Mykel Kochenderfer (Stanford University)
Zachary Manchester (Stanford)

More from the Same Authors