发表机构
Mondragon University（蒙德拉贡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出XAI引导的多目标进化搜索，结合多图像优化与积分梯度，提升DNN机器人导航系统鲁棒性测试的泛化性，成功率从53.85%提升至70%，并验证仿真到物理迁移的有效性。
AI 中文摘要
**背景：**深度神经网络（DNN）日益控制网络物理系统（CPS），但微小的输入扰动可能导致不安全的系统级行为。现有方法通常针对单个图像优化扰动，且仅在仿真中评估，限制了其泛化性和实际有效性。**目标：**本研究旨在生成在操作观测中仍保持有效的鲁棒性测试，并评估所产生的失败是否从仿真迁移到物理机器人。**方法：**我们提出一种可解释性引导的多目标进化方法，通过视觉和行为聚类选择代表性图像，生成稀疏扰动。聚合积分梯度引导变异朝向有影响力的图像区域。我们在Gazebo中对DNN控制的LeoRover进行评估，开展消融研究，并在物理机器人上验证分层扰动的子集。**结果：**该方法的中位成功率达到了70.0%，而无引导搜索为53.85%，中位超体积从0.65提升至0.73。多图像优化将成功率从50.0%提升至57.5%，而XAI引导进一步将其提升至70.0%。在仿真到现实评估中，仿真达到了0.95的精确率和0.67的召回率，仿真与物理失败时间显示出显著的正相关（0.617）。**结论：**将多图像优化与可解释性引导搜索相结合，可改进DNN控制机器人系统的鲁棒性测试。仿真能有效识别并优先排序可迁移的失败，但物理验证仍然必要，因为某些现实世界失败无法在仿真中复现。
英文摘要
**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.
