[Submitted on 24 Jul 2026]
Title:Generalizable Robustness Testing of DNN-Based Robotic Navigation Systems via XAI-Guided Search
View a PDF of the paper titled Generalizable Robustness Testing of DNN-Based Robotic Navigation Systems via XAI-Guided Search, by Khizra Sohail and 2 other authors
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Abstract:Context: Deep Neural Networks (DNNs) increasingly control Cyber-Physical Systems (CPSs), yet small input perturbations can cause unsafe system-level behavior. Existing approaches often optimize perturbations for individual images and evaluate them only in simulation, limiting their generalizability and practical validity.
Objectives: This work aims to generate robustness tests that remain effective across operational observations and to evaluate whether the resulting failures transfer from simulation to a physical robot.
Methods: We propose an explainability-guided multi-objective evolutionary approach that generates sparse perturbations over representative images selected through visual and behavioral clustering. Aggregated Integrated Gradients guide mutations toward influential image regions. We evaluate the approach on a DNN-controlled LeoRover in Gazebo, conduct an ablation study, and validate a stratified subset of perturbations on the physical robot.
Results: The approach achieved a median success rate of 70.0%, compared with 53.85% for unguided search, and increased median hypervolume from 0.65 to 0.73. Multi-image optimization improved the success rate from 50.0% to 57.5%, while XAI guidance further increased it to 70.0%. In the sim-to-real evaluation, simulation achieved 0.95 precision and 0.67 recall, and simulated and physical failure times showed a significant positive correlation of 0.617.
Conclusion: Combining multi-image optimization with explainability-guided search improves robustness testing for DNN-controlled robotic systems. Simulation effectively identifies and prioritizes transferable failures, but physical validation remains necessary because some real-world failures are not reproduced in simulation.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.06862 [cs.RO]
(or arXiv:2610.06862v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.06862
arXiv-issued DOI via DataCite
Submission history
From: Aitor Arrieta [view email] [v1] Fri, 24 Jul 2026 21:17:54 UTC (2,950 KB)
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