When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall \textit{system-level} competence of a robot as it operates in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

翻译：当测试条件与训练数据所代表的场景存在差异时，所谓分布外（OOD）输入会损害现代机器人自主栈中学习组件的可靠性。因此，应对分布外数据是实现可信赖的、基于学习的开放世界自主性的重要挑战。本文旨在阐释数据驱动机器人系统中分布外数据及其相关挑战这一主题，并与机器学习领域新兴的、独立研究分布外数据对学习模型影响的研究范式建立联系。我们认为，作为机器人学研究者，在机器人运行于分布外条件时应从整体"系统级"能力角度进行推理。我们围绕这一系统级视角下的分布外问题提出关键研究方向，以引导未来研究迈向安全可靠的学习驱动型自主系统。