確定的等価EMによる部分観測系のスケーラブルな同定
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
部分観測非線形系のオフライン同定において、確定的等価近似EMをブロック座標上昇として定式化し、高次元系でも効率的に学習できる手法を提案。曲技ヘリのダイナミクス同定で従来法を上回る予測性能を示した。
著者: Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester
分類: cs.LG, cs.RO, cs.SY, eess.SY, stat.ML
原文アブストラクト
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.
