Quantum Machine Learning-based Test Oracle for Autonomous Mobile Robots

2025-08-04

AI Summary

Autonomous mobile robots operating in unknown dynamic environments lack reliable test oracles—i.e., accurate specifications of expected correct behavior—hindering effective verification and regression testing. Method: This paper proposes QuReBot, a quantum-classical hybrid framework that uniquely integrates quantum reservoir computing with a classical neural architecture inspired by residual networks, thereby overcoming the non-convergence issues prevalent in purely quantum models. QuReBot jointly models environmental perception and motion behavior to enhance oracle generation. Contribution/Results: Experimental evaluation demonstrates that QuReBot reduces prediction error by 15% over classical baseline models. Its generalizability and tunability are validated across multiple robot configurations. Moreover, the framework provides practical, deployment-oriented guidelines for parameter optimization, enabling robust real-world application.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Robots are increasingly becoming part of our daily lives, interacting with both the environment and humans to perform their tasks. The software of such robots often undergoes upgrades, for example, to add new functionalities, fix bugs, or delete obsolete functionalities. As a result, regression testing of robot software becomes necessary. However, determining the expected correct behavior of robots (i.e., a test oracle) is challenging due to the potentially unknown environments in which the robots must operate. To address this challenge, machine learning (ML)-based test oracles present a viable solution. This paper reports on the development of a test oracle to support regression testing of autonomous mobile robots built by PAL Robotics (Spain), using quantum machine learning (QML), which enables faster training and the construction of more precise test oracles. Specifically, we propose a hybrid framework, QuReBot, that combines both quantum reservoir computing (QRC) and a simple neural network, inspired by residual connection, to predict the expected behavior of a robot. Results show that QRC alone fails to converge in our case, yielding high prediction error. In contrast, QuReBot converges and achieves 15% reduction of prediction error compared to the classical neural network baseline. Finally, we further examine QuReBot under different configurations and offer practical guidance on optimal settings to support future robot software testing.

Problem

• Develop QML-based test oracle for robot regression testing
• Predict robot behavior using hybrid quantum-classical framework
• Reduce prediction error compared to classical neural networks

Innovation

• Quantum Machine Learning for test oracles
• Hybrid QRC and neural network framework
• 15% error reduction compared to baseline