Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage Probably Approximately Correct (PAC)-Bayes theory to train a policy with a guaranteed bound on performance on the training distribution. Our idea for OOD detection relies on the following intuition: violation of the performance bound on test environments provides evidence that the robot is operating OOD. We formalize this via statistical techniques based on p-values and concentration inequalities. The approach provides guaranteed confidence bounds on OOD detection including bounds on both the false positive and false negative rates of the detector and is task-driven and only sensitive to changes that impact the robot's performance. We demonstrate our approach in simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in environments with wind disturbances and varied obstacle densities. Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials.

翻译：我们的目标是实现分布外（OOD）检测，即检测机器人是否在不同于训练分布的运行环境中工作。我们利用可能近似正确（PAC）-贝叶斯理论来训练一个策略，该策略在训练分布上具有保证的性能界限。我们关于OOD检测的思路基于以下直觉：在测试环境中违反性能界限将提供机器人处于分布外运行的证据。我们通过基于p值和集中不等式的统计技术将其形式化。该方法提供了OOD检测的保证置信界限，包括检测器假阳性率和假阴性率的界限，且是任务驱动的，仅对影响机器人性能的变化敏感。我们通过仿真和硬件实验展示了该方法，包括使用不熟悉形状或姿态的物体进行抓取任务，以及无人机在存在风干扰和不同障碍物密度的环境中执行基于视觉的避障任务。我们的实验表明，我们仅需少量试验即可实现任务驱动的OOD检测。