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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.22182 |
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| _version_ | 1866917358256259072 |
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| author | Sawant, Omkar Zanatta, Luca Malczyk, Grzegorz Alexis, Kostas |
| author_facet | Sawant, Omkar Zanatta, Luca Malczyk, Grzegorz Alexis, Kostas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22182 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements Sawant, Omkar Zanatta, Luca Malczyk, Grzegorz Alexis, Kostas Robotics 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. |
| title | Cross-Modal Reinforcement Learning for Navigation with Degraded Depth Measurements |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.22182 |