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). We demonstrate our approach in both simulation and hardware for a visual servoing task, and show that our method indeed issues an alert before a failure occurs.

翻译：在将现代机器学习赋能的机器人系统部署于高风险应用时，检测分布漂移至关重要。然而，现有的多数分布漂移检测方法并不适用于机器人场景——此类场景中数据常以流式方式到达且可能具有极高维度。本文提出一种具备虚警率保证的在线分布漂移检测方法：当不存在分布漂移时，系统以极大概率（概率$< \epsilon$）不会误发警报；因此，任何触发的警报均应被重视。该方法专为高维数据的高效检测而设计，在现实机器人场景中相比现有工作可实现高达11倍的检测速度提升，同时在实际应用中保持较低的漏检率（实验表明，只要存在分布漂移，该方法确实会触发警报）。我们通过视觉伺服任务的仿真与硬件实验验证了该方法，结果表明该方法确实能在故障发生前发出警报。