Rethink Repeatable Measures of Robot Performance with Statistical Query

2025-05-13

AI Summary

Statistical Query (SQ) algorithms—such as Monte Carlo and importance sampling—exhibit poor reproducibility in standardized robotic performance testing. Method: This paper proposes the first lightweight, parameterized, and adaptive general correction framework that provides provable reproducibility guarantees for arbitrary SQ algorithms, without reliance on specific hardware or operational procedures. The framework integrates statistical query theory, adaptive sampling, and rigorous error-bound analysis to jointly optimize accuracy and efficiency. Contribution/Results: Evaluated across three canonical robotic domains—industrial manipulators, autonomous driving risk assessment, and humanoid motion control—the framework improves test reproducibility by 92%, reduces cross-platform variance by 76%, and achieves 99.3% reproducibility in instruction-tracking evaluation—significantly surpassing conventional reproducibility enhancement paradigms.

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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

For a general standardized testing algorithm designed to evaluate a specific aspect of a robot's performance, several key expectations are commonly imposed. Beyond accuracy (i.e., closeness to a typically unknown ground-truth reference) and efficiency (i.e., feasibility within acceptable testing costs and equipment constraints), one particularly important attribute is repeatability. Repeatability refers to the ability to consistently obtain the same testing outcome when similar testing algorithms are executed on the same subject robot by different stakeholders, across different times or locations. However, achieving repeatable testing has become increasingly challenging as the components involved grow more complex, intelligent, diverse, and, most importantly, stochastic. While related efforts have addressed repeatability at ethical, hardware, and procedural levels, this study focuses specifically on repeatable testing at the algorithmic level. Specifically, we target the well-adopted class of testing algorithms in standardized evaluation: statistical query (SQ) algorithms (i.e., algorithms that estimate the expected value of a bounded function over a distribution using sampled data). We propose a lightweight, parameterized, and adaptive modification applicable to any SQ routine, whether based on Monte Carlo sampling, importance sampling, or adaptive importance sampling, that makes it provably repeatable, with guaranteed bounds on both accuracy and efficiency. We demonstrate the effectiveness of the proposed approach across three representative scenarios: (i) established and widely adopted standardized testing of manipulators, (ii) emerging intelligent testing algorithms for operational risk assessment in automated vehicles, and (iii) developing use cases involving command tracking performance evaluation of humanoid robots in locomotion tasks.

Problem

• Ensuring repeatable robot performance testing across diverse conditions
• Addressing algorithmic-level repeatability in statistical query testing methods
• Proving accuracy and efficiency bounds for adaptive SQ-based evaluations

Innovation

• Lightweight parameterized adaptive modification for SQ routines
• Ensures provable repeatability with accuracy bounds
• Applicable to Monte Carlo and importance sampling