CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots

Robotic systems have several subsystems that possess a huge combinatorial configuration space and hundreds or even thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters can be tailored to target specific objectives, but when incorrectly configured, can cause functional faults. 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, which abstracts the causal relationships between various configuration options and the robot's performance objectives. We demonstrate CaRE's efficacy by finding the root cause of the observed functional faults via CaRE and validating the diagnosed root cause, 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 (simulating Husky in Gazebo) are transferable to physical robots across different platforms (Turtlebot 3).