When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables, i.e., regression tasks. However, in practice, robotic systems are also subject to discrete, external environmental changes, e.g., having to carry objects of certain weights or operating on frozen, wet, or dry surfaces. Such influences can be modeled as discrete context variables. In the existing literature, such contexts are, if considered, mostly assumed to be known. In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables. To achieve this, we derive frequentist guarantees for multi-class classification, allowing us to estimate the current context from measurements. Further, we propose an approach for identifying contexts through experiments. We discuss under which conditions we can retain theoretical guarantees and demonstrate the applicability of our algorithm on a Furuta pendulum with camera measurements of different weights that serve as contexts.

翻译：在现实世界中部署机器学习算法时，保证安全性是一项至关重要的事项。现有的安全学习方法通常考虑连续变量，即回归任务。然而，在实践应用中，机器人系统还面临离散的外部环境变化，例如必须携带特定重量的物体，或在结冰、潮湿或干燥的表面上运行。此类影响可建模为离散的上下文变量。在现有文献中，即便考虑了这种上下文，通常也被假设为已知。在本工作中，我们放弃了这一假设，并展示了如何在无法直接测量上下文变量的情况下实现安全学习。为此，我们推导了多类分类的频率学派保证，从而能够通过测量值估计当前上下文。此外，我们提出了一种通过实验识别上下文的方法。我们讨论了在何种条件下能够保留理论保证，并在以不同重量物体作为上下文的Furuta摆相机测量实验中展示了我们算法的适用性。