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。

基于领域信息的高斯过程状态空间模型的区分外分布检测

Out of Distribution Detection via Domain-Informed Gaussian Process State  Space Models

Out-of-training-distribution (OOD) scenarios are a common challenge of
learning agents at deployment, typically leading to arbitrary deductions and
poorly-informed decisions. In principle, detection of and adaptation to OOD
scenes can mitigate their adverse effects. In this paper, we highlight the
limitations of current approaches to novel driving scenes and propose an
epistemic uncertainty-aware planning method, called \emph{robust imitative
planning} (RIP). Our method can detect and recover from some distribution
shifts, reducing the overconfident and catastrophic extrapolations in OOD
scenes. If the model's uncertainty is too great to suggest a safe course of
action, the model can instead query the expert driver for feedback, enabling
sample-efficient online adaptation, a variant of our method we term
\emph{adaptive robust imitative planning} (AdaRIP). Our methods outperform
current state-of-the-art approaches in the nuScenes \emph{prediction}
challenge, but since no benchmark evaluating OOD detection and adaption
currently exists to assess \emph{control}, we introduce an autonomous car
novel-scene benchmark, \texttt{CARNOVEL}, to evaluate the robustness of driving
agents to a suite of tasks with distribution shifts.

本文提出了一种新的方法，名称为稳健仿真规划（RIP），可以检测和适应一些分布的偏移，减少 OOD 场景中的自信和灾难性外推。如果模型的不确定性太高，建议采用反馈机制，同时引入一个自动驾驶汽车新场景基准测试（CARNOVEL）来评估驾驶代理的鲁棒性。