Many practitioners in robotics regularly depend on classic, hand-designed algorithms. Often the performance of these algorithms is tuned across a dataset of annotated examples which represent typical deployment conditions. Automatic tuning of these settings is traditionally known as algorithm configuration. In this work, we extend algorithm configuration to automatically discover multiple modes in the tuning dataset. Unlike prior work, these configuration modes represent multiple dataset instances and are detected automatically during the course of optimization. We propose three methods for mode discovery: a post hoc method, a multi-stage method, and an online algorithm using a multi-armed bandit. Our results characterize these methods on synthetic test functions and in multiple robotics application domains: stereoscopic depth estimation, differentiable rendering, motion planning, and visual odometry. We show the clear benefits of detecting multiple modes in algorithm configuration space.

翻译：许多机器人领域的从业者通常依赖经典的、手工设计的算法。这些算法的性能往往需要通过一组代表典型部署条件的带标注示例数据集进行调整。传统上，这种自动调整设置的过程被称为算法配置。在本工作中，我们将算法配置扩展为自动发现调优数据集中的多种模式。与先前工作不同，这些配置模式代表了多个数据集实例，并在优化过程中自动被检测到。我们提出了三种模式发现方法：事后方法、多阶段方法以及使用多臂老虎机（multi-armed bandit）的在线算法。我们在合成测试函数以及多个机器人应用领域中对这些方法进行了特征分析：立体深度估计、可微分渲染、运动规划以及视觉里程计。我们展示了在算法配置空间中检测多种模式的明确优势。