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.

提出了一种名为 CURE 的方法，通过识别因果关系的相关配置选项，使优化过程在较小的搜索空间中运行，从而加快了机器人性能的优化。

CURE：机器人中的模拟增强自动调优

CURE: Simulation-Augmented Auto-Tuning in Robotics

Modern software systems are usually highly configurable, providing users with
customized functionality through various configuration options. Understanding
how system performance varies with different option combinations is important
to determine optimal configurations that meet specific requirements. Due to the
complex interactions among multiple options and the high cost of performance
measurement under a huge configuration space, it is challenging to study how
different configurations influence the system performance. To address these
challenges, we propose HINNPerf, a novel hierarchical interaction neural
network for performance prediction of configurable systems. HINNPerf employs
the embedding method and hierarchic network blocks to model the complicated
interplay between configuration options, which improves the prediction accuracy
of the method. Besides, we devise a hierarchical regularization strategy to
enhance the model robustness. Empirical results on 10 real-world configurable
systems show that our method statistically significantly outperforms
state-of-the-art approaches by achieving average 22.67% improvement in
prediction accuracy. In addition, combined with the Integrated Gradients
method, the designed hierarchical architecture provides some insights about the
interaction complexity and the significance of configuration options, which
might help users and developers better understand how the configurable system
works and efficiently identify significant options affecting the performance.

通过使用 HINNPerf 模型，结合嵌入方法和分层网络块，提高了可配置系统性能预测的准确度并设计了一种层次正则化策略，使其更容易理解和识别影响性能的重要配置选项。实证结果显示在真实可配置系统上，该方法比现有的方法平均提高 22.67％的预测精度，并通过集成梯度发现了配置选项的相互作用复杂性和重要性，从而帮助用户和开发人员更好地了解可配置系统。

HINNPerf: 可配置系统性能预测的分层交互神经网络

HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems

Modern software systems provide many configuration options which
significantly influence their non-functional properties. To understand and
predict the effect of configuration options, several sampling and learning
strategies have been proposed, albeit often with significant cost to cover the
highly dimensional configuration space. Recently, transfer learning has been
applied to reduce the effort of constructing performance models by transferring
knowledge about performance behavior across environments. While this line of
research is promising to learn more accurate models at a lower cost, it is
unclear why and when transfer learning works for performance modeling. To shed
light on when it is beneficial to apply transfer learning, we conducted an
empirical study on four popular software systems, varying software
configurations and environmental conditions, such as hardware, workload, and
software versions, to identify the key knowledge pieces that can be exploited
for transfer learning. Our results show that in small environmental changes
(e.g., homogeneous workload change), by applying a linear transformation to the
performance model, we can understand the performance behavior of the target
environment, while for severe environmental changes (e.g., drastic workload
change) we can transfer only knowledge that makes sampling more efficient,
e.g., by reducing the dimensionality of the configuration space.

本文在四个软件系统中进行了实验研究，以了解何时在性能建模中应用迁移学习有益。 结果表明，在环境变化较小的情况下，我们可以通过对性能模型应用线性变换来理解目标环境的性能行为，而在环境变化严重的情况下，我们只能迁移使采样更有效的知识，例如通过降低配置空间的维数。