Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts. However, system designers must understand the cause of a distribution shift in order to implement the appropriate intervention or mitigation strategy and prevent system failure. In this paper, we present a novel framework for diagnosing distribution shifts in a streaming fashion by deploying multiple stochastic martingales simultaneously. We show that knowledge of the underlying cause of a distribution shift can lead to proper interventions over the lifecycle of a deployed system. Our experimental framework can easily be adapted to different types of distribution shifts, models, and datasets. We find that our method outperforms existing work on diagnosing distribution shifts in terms of speed, accuracy, and flexibility, and validate the efficiency of our model in both simulated and live hardware settings.

翻译：在安全关键型机器人环境中部署的机器学习系统必须对分布偏移具有鲁棒性。然而，系统设计者需要理解分布偏移的根本原因，才能实施适当的干预或缓解策略，从而防止系统故障。本文提出了一种新颖的框架，通过同时部署多个随机鞅来以流式方式诊断分布偏移。我们证明，了解分布偏移的根本原因有助于在已部署系统的生命周期内采取恰当的干预措施。我们的实验框架可以轻松适应不同类型的分布偏移、模型和数据集。实验结果表明，本方法在诊断分布偏移的速度、准确性和灵活性方面均优于现有工作，并在仿真和实时硬件环境中验证了模型的高效性。