ドメイン知識を組み込んだガウス過程状態空間モデルによる分布外検出
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
シミュレーションやノミナルモデルから得たドメイン知識をガウス過程のカーネルに埋め込み、予測に基づくオンライン分布外検出モニタを構築。実機四足歩行ロボットの未知地形分類で有効性を示した。
著者: Alonso Marco, Elias Morley, Claire J. Tomlin
分類: cs.RO, cs.LG
原文アブストラクト
In order for robots to safely navigate in unseen scenarios using learning-based methods, it is important to accurately detect out-of-training-distribution (OoD) situations online. Recently, Gaussian process state-space models (GPSSMs) have proven useful to discriminate unexpected observations by comparing them against probabilistic predictions. However, the capability for the model to correctly distinguish between in- and out-of-training distribution observations hinges on the accuracy of these predictions, primarily affected by the class of functions the GPSSM kernel can represent. In this paper, we propose (i) a novel approach to embed existing domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on receding-horizon predictions. Domain knowledge is provided in the form of a dataset, collected either in simulation or by using a nominal model. Numerical results show that the informed kernel yields better regression quality with smaller datasets, as compared to standard kernel choices. We demonstrate the effectiveness of the OoD monitor on a real quadruped navigating an indoor setting, which reliably classifies previously unseen terrains.
