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，检测分布偏移至关重要。然而，现有多数分布偏移检测方法并不适用于机器人场景，该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偏移检测方法，该方法能保证虚警率——即当不存在分布偏移时，系统极不可能（概率$< \epsilon$）误发警报；因此任何警报都应被重视。该方法专为高效检测设计，即使面对高维数据仍能保持性能，在真实机器人场景中相比先前工作实现高达11倍的检测速度提升，同时实践中保持低漏报率（在我们的实验中，每当发生分布偏移时，该方法确实会发出警报）。我们分别在仿真环境和真实硬件上通过视觉伺服任务验证了该方法，实验表明该方法确实能在故障发生前发出警报。