入力不確実性を考慮した空中ナビゲーション向け深層ニューラルネットワークのロバスト性向上
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
空中ナビゲーションのベイズ深層学習制御ポリシーにおいて、入力側の不確実性を考慮することで分布外シナリオでのロバスト性を高める手法を提案した。
著者: Fabio Arnez, Huascar Espinoza, Ansgar Radermacher, François Terrier
分類: cs.RO
原文アブストラクト
Uncertainty quantification methods are required in autonomous systems that include deep learning (DL) components to assess the confidence of their estimations. However, to successfully deploy DL components in safety-critical autonomous systems, they should also handle uncertainty at the input rather than only at the output of the DL components. Considering a probability distribution in the input enables the propagation of uncertainty through different components to provide a representative measure of the overall system uncertainty. In this position paper, we propose a method to account for uncertainty at the input of Bayesian Deep Learning control policies for Aerial Navigation. Our early experiments show that the proposed method improves the robustness of the navigation policy in Out-of-Distribution (OoD) scenarios.
