CURE: Simulation-Augmented Auto-Tuning in Robotics

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.

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).