データ駆動型反復線形二次レギュレータによる不確実性下の制御学習
Learning to Control under Uncertainty with Data-Based Iterative Linear Quadratic Regulator
プロセスとセンシングの不確実性がある動的システムに対し、データに基づくiLQRの有効性を解析し、完全観測では大域的最適に収束するが部分観測ではバイアスが生じることを示し、複数ロールアウトの平均化で真の最適解が得られることをロボット例で検証した。
著者: Ran Wang, Raman Goyal, Suman Chakravorty
分類: cs.RO, cs.SY, eess.SY, math.DS, math.OC
原文アブストラクト
This paper studies the learning-to-control problem under process and sensing uncertainties for dynamical systems. In our previous work, we developed a data-based generalization of the iterative linear quadratic regulator (iLQR) to design closed-loop feedback control for high-dimensional dynamical systems with partial state observation. This method required perfect simulation rollouts which are not realistic in real applications. In this work, we briefly introduce this method and explore its efficacy under process and sensing uncertainties. We prove that in the fully observed case where the system dynamics are corrupted with noise but the measurements are perfect, it still converges to the global minimum. However, in the partially observed case where both process and measurement noise exist in the system, this method converges to a biased "optimum". Thus multiple rollouts need to be averaged to retrieve the true optimum. The analysis is verified in two nonlinear robotic examples simulated in the above cases.
