Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation

2025-06-25

AI Summary

Real-world validation of learned robotic systems incurs high costs and suffers from scarce empirical data, making it difficult to establish high-confidence performance guarantees. To address this, we propose a variance-reduction estimation framework leveraging cross-platform paired data—specifically, introducing the control variates method into robotic performance evaluation for the first time. By exploiting strong correlations between simulated and real-world observations, our approach constructs auxiliary estimators that, when combined with Monte Carlo estimation, enable theoretically controllable variance reduction. We validate the method on autonomous driving and quadrupedal locomotion tasks, demonstrating over 50% reduction in required real-world samples at equivalent confidence levels—significantly improving sample efficiency and lowering validation costs. Our core contribution is the systematic establishment of a control-variates-based evaluation paradigm tailored to learning-based robotic systems, uniquely balancing theoretical rigor with practical engineering applicability.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Learning-based robotic systems demand rigorous validation to assure reliable performance, but extensive real-world testing is often prohibitively expensive, and if conducted may still yield insufficient data for high-confidence guarantees. In this work, we introduce a general estimation framework that leverages paired data across test platforms, e.g., paired simulation and real-world observations, to achieve better estimates of real-world metrics via the method of control variates. By incorporating cheap and abundant auxiliary measurements (for example, simulator outputs) as control variates for costly real-world samples, our method provably reduces the variance of Monte Carlo estimates and thus requires significantly fewer real-world samples to attain a specified confidence bound on the mean performance. We provide theoretical analysis characterizing the variance and sample-efficiency improvement, and demonstrate empirically in autonomous driving and quadruped robotics settings that our approach achieves high-probability bounds with markedly improved sample efficiency. Our technique can lower the real-world testing burden for validating the performance of the stack, thereby enabling more efficient and cost-effective experimental evaluation of robotic systems.

Problem

• Reducing real-world testing costs for robotic systems
• Improving metric estimation via cross-platform data correlation
• Enhancing sample efficiency in performance validation

Innovation

• Leveraging paired data across test platforms
• Using control variates for variance reduction
• Improving sample efficiency with theoretical guarantees