Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
Abstract
In order for robots to safely navigate in unseen scenarios, it is desired to rely on algorithms capable of efficiently detecting out-of-training-distribution (OoD) situations online and with accuracy. 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 discriminate hinges on the accuracy of these predictions, which are primarily affected by the choice of the GP kernel, which restrains the family of functions the model can represent. In this paper, we propose a novel approach to construct an informed kernel for the GPSSM using existing domain knowledge. The resulting kernel is non-stationary and the construction procedure only requires access to a (potentially) incorrect nominal model or a simulator. Numerical results show that this kernel outperforms standard kernel choices. We use this method to detect OoD situations on a real quadruped navigating an indoors setting with changing terrains.
