すべての行動は平等ではない：安全で最適な非線形システム同定のためのベイズ最適実験計画
Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification
非線形システムのパラメータ同定において、安全性を保証しつつ最大の情報を得る軌道をベイズ最適実験計画で設計し、従来法より桁違いに少ないサンプルで高精度な同定を実現した。
著者: Parker Ewen, Gitesh Gunjal, Joey Wilson, Jinsun Liu, Challen Enninful Adu, Ram Vasudevan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Uncertainty in state or model parameters is common in robotics and typically handled by acquiring system measurements that yield information about the uncertain quantities of interest. Inputs to a nonlinear dynamical system yield outcomes that produce varying amounts of information about the underlying uncertain parameters of the system. To maximize information gained with respect to these uncertain parameters we present a Bayesian approach to data collection for system identification called Bayesian Optimal Experimental Design (BOED). The formulation uses parameterized trajectories and cubature to compute maximally informative system trajectories which obtain as much information as possible about unknown system parameters while also ensuring safety under mild assumptions. The proposed method is applicable to non-linear and non-Gaussian systems and is applied to a high-fidelity vehicle model from the literature. It is shown the proposed approach requires orders of magnitude fewer samples compared to state-of-the-art BOED algorithms from the literature while simultaneously providing safety guarantees.
