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

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