Search-based Robustness Testing of Laptop Refurbishing Robotic Software

2026-05-08

AI Summary

This work proposes PROBE, a method designed to enhance the robustness of object detection models in laptop refurbishment robots against subtle perturbations that could otherwise lead to misdetections and equipment damage. PROBE uniquely integrates search-based multi-objective optimization—using the NSGA-II algorithm—with localized adversarial perturbation generation, jointly optimizing perturbation magnitude, detection confidence, and localization accuracy to systematically explore the perturbation space and expose model failures. The approach is 3–7 times more efficient than random search, induces model failures with smaller perturbations, and demonstrates strong cross-model transferability. Additionally, PROBE introduces metamorphic relations to evaluate model stability even in non-failure scenarios, offering a comprehensive assessment of detector reliability under realistic operating conditions.

Abstract

The Danish Technological Institute (DTI) focuses on transferring advanced technologies (including robots) to the industry and the public sector. One key application is laptop refurbishment using specialized robots, aimed at promoting reuse, reducing electronic waste, and supporting the European Circular Economy Action Plan. The software of such robots often includes features that use object detection models to detect objects for various purposes, such as identifying screws for laptop disassembly or detecting stickers to remove them. Ensuring the robustness of such models to small input variations remains a critical challenge, and addressing it is important to avoid potential damage to laptops during refurbishment. In this paper, we propose PROBE, a search-based robustness testing approach that leverages multi-objective optimization to identify minimal, localized perturbations that expose failures in object detection models used in the software of laptop refurbishing robots. PROBE employs NSGA-II to systematically explore the perturbation space, optimizing for failure induction considering both localization and confidence, and perturbation magnitude, while enabling the discovery of diverse failure cases. Results show that PROBE is 3$\times$ to 7$\times$ more effective than random search in generating failure-inducing perturbations, while requiring smaller perturbation magnitudes, and that the generated perturbations transfer across models. We further show that metamorphic relations provide additional insights into model robustness, enabling the assessment of stability even in non-failing cases.

Problem

• robustness
• object detection
• laptop refurbishing
• input perturbations
• robotic software

Innovation

• search-based testing
• robustness testing
• object detection
• multi-objective optimization
• metamorphic relations