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arXiv · 2603.22182

Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements

Omkar Sawant·Luca Zanatta·Grzegorz Malczyk·Kostas Alexis
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Abstract

This paper presents a cross-modal learning framework that exploits complementary information from depth and grayscale images for robust navigation. We introduce a Cross-Modal Wasserstein Autoencoder that learns shared latent representations by enforcing cross-modal consistency, enabling the system to infer depth-relevant features from grayscale observations when depth measurements are corrupted. The learned representations are integrated with a Reinforcement Learning-based policy for collision-free navigation in unstructured environments when depth sensors experience degradation due to adverse conditions such as poor lighting or reflective surfaces. Simulation and real-world experiments demonstrate that our approach maintains robust performance under significant depth degradation and successfully transfers to real environments.

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Omkar Sawant, Luca Zanatta, Grzegorz Malczyk, Kostas Alexis. 2026-03-23. Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements. https://arxiv.org/abs/2603.22182

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