QML-Based Test Oracle Supports Regression Testing of Autonomous Mobile Robots

As robots become increasingly integrated into everyday life, ensuring the reliability of their software through rigorous testing is paramount, yet defining what constitutes ‘correct’ behaviour for a robot operating in unpredictable environments presents a significant challenge. Xinyi Wang from Simula Research Laboratory and University of Oslo, Qinghua Xu from Lero Research Centre and University of Limerick, and Paolo Arcaini from the National Institute of Informatics, alongside their colleagues, address this issue by developing a novel test oracle powered by quantum machine learning. Their research introduces QuReBot, a hybrid framework combining reservoir computing and neural networks, to predict expected robot behaviour and facilitate regression testing of autonomous mobile robots built by PAL Robotics. The team demonstrates that QuReBot not only converges successfully where traditional quantum methods fail, but also achieves a 15% reduction in prediction error compared to standard neural network approaches, offering a promising step towards more robust and dependable robotic systems.

Robots are increasingly integrated into daily life, interacting with both environments and humans to perform tasks. Consequently, robot software frequently undergoes upgrades to add new functionalities, fix bugs, or remove obsolete features. This necessitates regression testing to ensure continued correct operation, but determining the expected correct behaviour of robots presents a significant challenge. This difficulty arises because robots must operate in potentially unknown environments, making it hard to define what constitutes correct behaviour. Machine learning (ML)-based test oracles offer a viable solution to this problem, learning acceptable behaviour from observed data rather than relying on pre-programmed expectations.

Quantum Reservoir Computing Generates Robotic Test Oracles

This document details a research paper introducing QuReBot, a novel approach to testing robotic systems using Quantum Reservoir Computing (QRC) to generate test oracles. The core challenge in robotic testing lies in accurately predicting a robot’s expected behaviour, particularly in complex and unpredictable situations. This research addresses this challenge by leveraging the power of quantum computing to create more intelligent and reliable test oracles. The team proposes QuReBot, which utilizes Quantum Reservoir Computing (QRC) to learn the expected behaviour of a robotic system from training data, such as system logs and sensor readings.

The trained QRC then acts as a test oracle, predicting the expected output for given inputs. QRC is particularly well-suited for handling the time-series data common in robotics, like sensor readings over time and motor commands. The system learns from data representing normal robotic operation, building a model of the system’s dynamics. Test cases are created with various inputs, and the trained QRC predicts the expected output for each case. The actual output of the robotic system is then compared to the QRC’s prediction to determine if the test passes or fails. This approach offers several benefits, including improved oracle accuracy, the ability to handle complex behaviours, adaptability to evolving robotic systems, and the potential for early bug detection. Thorough testing requires determining the expected behaviour of a robot in varied and unpredictable environments, a task traditionally difficult and resource-intensive. This work introduces QuReBot, a new approach that leverages the principles of quantum computing to create more accurate and efficient test oracles, systems that predict the correct behaviour of the robot during testing. The core of QuReBot is a hybrid framework combining quantum reservoir computing (QRC) with elements of traditional neural networks.

QRC utilizes the unique properties of quantum systems to process information with minimal training, potentially offering significant advantages over conventional machine learning methods. To overcome limitations in applying QRC directly to complex robotic data, the researchers integrated a “residual connection”, a technique borrowed from deep learning. This connection allows the system to bypass the quantum processing component when appropriate, providing a more stable and accurate prediction. The resulting QuReBot system demonstrably outperforms both standalone QRC and conventional neural networks, achieving a 15% reduction in prediction error.

This improvement signifies a substantial step forward in the ability to automatically verify the correctness of robot software updates and new functionalities. The team tested QuReBot using robots developed by PAL Robotics, focusing on the navigation software that allows the robots to move safely and efficiently in dynamic environments. By accurately predicting the expected state of the robot, QuReBot enables developers to identify and address potential issues before deployment, ultimately enhancing the reliability and safety of these increasingly prevalent machines.

QuReBot Predicts Robot Behaviour with Reservoir Computing

This research presents QuReBot, a novel framework designed to improve regression testing for autonomous mobile robots. The team successfully combined reservoir computing with a simple neural network, inspired by residual connections, to predict expected robot behaviour. Results demonstrate that QuReBot converges effectively and achieves a 15% reduction in prediction error compared to a traditional neural network baseline, addressing the challenge of creating reliable test oracles for robots operating in dynamic environments. The study represents the first exploration of applying quantum algorithms to test an industrial robot within a real-world context, specifically using data collected from a PAL Robotics TIAGo OMNI robot in an office setting.

While the current work focuses on one robot model, the data-driven and hardware-agnostic nature of QuReBot suggests potential applicability to other robotic systems. The authors acknowledge limitations related to the inherent randomness of the machine learning models used and plan to investigate the influence of alternative machine learning methods within the residual connection branch. Future research will also focus on assessing the generalizability of QuReBot across various scenarios and robot types.