When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical. However, most existing methods for detecting distribution shift are not well-suited to robotics settings, where data often arrives in a streaming fashion and may be very high-dimensional. In this work, we present an online method for detecting distribution shift with guarantees on the false positive rate - i.e., when there is no distribution shift, our system is very unlikely (with probability $< \epsilon$) to falsely issue an alert; any alerts that are issued should therefore be heeded. Our method is specifically designed for efficient detection even with high dimensional data, and it empirically achieves up to 11x faster detection on realistic robotics settings compared to prior work while maintaining a low false negative rate in practice (whenever there is a distribution shift in our experiments, our method indeed emits an alert).

翻译：在高风险应用中部署现代基于机器学习的机器人系统时，检测分布漂移至关重要。然而，现有的大多数分布漂移检测方法并不适合机器人应用场景——在这些场景中，数据通常以流式方式到达且可能具有极高维度。本文提出一种在线分布漂移检测方法，该方法对误报率具有保证——即当不存在分布漂移时，系统极不可能（概率小于 $\epsilon$）错误触发警报；因此任何发出的警报都应引起重视。我们的方法专为高效检测而设计，即便面对高维数据也能保持性能。在现实机器人场景中，与先前工作相比，该方法在实验中实现最高达11倍的检测加速，同时在实际应用中保持低漏报率（每当实验中存在分布漂移时，我们的方法确实会发出警报）。