ニューラルネットワーク制御器を持つ閉ループ系の効率的な到達可能性解析
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers
NN制御器付き閉ループ系の前方到達集合を凸最適化で推定する手法を提案し、入力集合分割で精度を補い、従来法より10倍低保守的かつ高速に計算できることを示した。
著者: Michael Everett, Golnaz Habibi, Jonathan P. How
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems' safety properties. In particular, this work focuses on estimating the forward reachable set of closed-loop systems with NN controllers. Recent work provides bounds on these reachable sets, yet the computationally efficient approaches provide overly conservative bounds (thus cannot be used to verify useful properties), whereas tighter methods are too intensive for online computation. This work bridges the gap by formulating a convex optimization problem for reachability analysis for closed-loop systems with NN controllers. While the solutions are less tight than prior semidefinite program-based methods, they are substantially faster to compute, and some of the available computation time can be used to refine the bounds through input set partitioning, which more than overcomes the tightness gap. The proposed framework further considers systems with measurement and process noise, thus being applicable to realistic systems with uncertainty. Finally, numerical comparisons show $10\times$ reduction in conservatism in $\frac{1}{2}$ of the computation time compared to the state-of-the-art, and the ability to handle various sources of uncertainty is highlighted on a quadrotor model.
