Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e.g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact non-trivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e.g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e.g., optimization steps, allotted time) and lacking transferability. This paper proposes CURE -- a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e.g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation.

翻译：机器人系统通常由多个子系统（如定位和导航）组成，每个子系统包含大量可配置组件（例如选择不同的规划算法）。为组件选定算法后，还需将其关联的配置选项设置为适当值。系统栈中各配置选项之间存在复杂的非平凡交互。由于软硬件配置选项的相互作用导致配置空间呈指数级增长且高度复杂，为高度可配置的机器人寻找最优配置以实现预期性能面临重大挑战。此外，不同环境和机器人平台之间的可迁移性需求进一步加剧了这些挑战。数据高效优化算法（如贝叶斯优化）已越来越多地用于自动化信息物理系统中的可配置参数调优。然而，此类优化算法在后期收敛时往往已耗尽分配预算（如优化步数、时间限制），且缺乏可迁移性。本文提出CURE方法——通过识别因果相关的配置选项，使优化过程能够在缩减的搜索空间内运行，从而加速机器人性能优化。CURE通过在源环境（如Gazebo模拟器等低成本环境）中学习因果模型来抽象配置选项与机器人性能目标之间的因果关系，并将所学知识应用于目标环境（如Turtlebot 3物理机器人）的优化。我们通过在物理机器人和仿真中开展不同程度部署变化的实验，证明了CURE的有效性与可迁移性。