Robotic systems have subsystems with a combinatorially large configuration space and hundreds or thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters are set to target specific objectives, but they can cause functional faults when incorrectly configured. Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance. This paper proposes CaRE -- a method for diagnosing the root cause of functional faults through the lens of causality. CaRE abstracts the causal relationships between various configuration options and the robot's performance objectives by learning a causal structure and estimating the causal effects of options on robot performance indicators. We demonstrate CaRE's efficacy by finding the root cause of the observed functional faults and validating the diagnosed root cause by conducting experiments in both physical robots (Husky and Turtlebot 3) and in simulation (Gazebo). Furthermore, we demonstrate that the causal models learned from robots in simulation (e.g., Husky in Gazebo) are transferable to physical robots across different platforms (e.g., Husky and Turtlebot 3).

翻译：机器人系统包含具有组合爆炸式配置空间的子系统，其数百甚至数千个软硬件配置选项间存在复杂交互。配置参数旨在实现特定目标，但错误配置会导致功能故障。由于配置空间呈指数级增长，且机器人的配置设置与性能之间存在依赖关系，定位此类故障的根本原因极具挑战性。本文提出CaRE——一种基于因果视角诊断功能故障根本原因的方法。CaRE通过学习因果结构并估算配置选项对机器人性能指标的影响效果，抽象描述各配置选项与机器人性能目标之间的因果关系。我们通过在实体机器人（Husky和Turtlebot3）及仿真环境（Gazebo）中开展实验，定位功能故障的根本原因并验证诊断结果，从而证明CaRE的有效性。此外，我们展示了从仿真机器人（如Gazebo中的Husky）中习得的因果模型具有跨平台可迁移性，可应用于不同物理机器人平台（如Husky和Turtlebot3）。