CURE: Simulation-Augmented Auto-Tuning in Robotics
CoRR(2024)
摘要
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.
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