Generalizable Robustness Testing of DNN-Based Robotic Navigation Systems via XAI-Guided Search

arXiv:2610.06862v1 Announce Type: new 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.

Source: arXiv cs.RO | 2026-10-07