CURE: Simulation-Augmented Auto-Tuning in Robotics

2024-02-08

arXiv.org

AI Summary

Highly configurable robotic systems suffer from poor tuning efficiency, low sample efficiency, and weak cross-platform transferability due to strong hardware-software configuration coupling. Method: This paper proposes a causal discovery–based configuration reduction and transfer optimization framework. It innovatively applies the PC algorithm and do-calculus to learn configuration–performance causal models in low-cost simulation environments (e.g., Gazebo), then transfers the learned causal structure to real robots (e.g., TurtleBot 3) via simulation–real-world joint modeling and causal-aware transfer learning. Contribution/Results: By unifying causal discovery, Bayesian optimization, and transfer learning, the framework significantly improves optimization efficiency: multi-environment, multi-platform experiments demonstrate over 40% reduction in convergence steps while maintaining high-precision performance transfer—validating its data efficiency and strong generalization capability across platforms.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Causal Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Optimize robot configurations efficiently
• Address complex configuration interactions
• Ensure transferability across environments

Innovation

• Causal model learning
• Reduced search space optimization
• Simulation-to-real transferability