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 assumed given as a dataset
collected either in simulation or 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.

提出了一种利用现有领域知识嵌入核函数的新方法，以及一种基于滚动预测的离线在线监控器，用于使机器人在未知情况下使用基于学习的方法安全导航。数值结果表明，与标准的核函数选择相比，具有领域知识的核函数在小数据集上具有更好的回归质量。通过对一个室内设置中的四足机器人进行测试，演示了 OoD 监控器的有效性，可可靠地对以前未见的地形进行分类。