Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908643748741120 |
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| author | Pushp, Durgakant Chen, Weizhe Chen, Zheng Luo, Chaomin Gregory, Jason M. Liu, Lantao |
| author_facet | Pushp, Durgakant Chen, Weizhe Chen, Zheng Luo, Chaomin Gregory, Jason M. Liu, Lantao |
| contents | Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings, map-based approaches demand heavy mapping effort, and learning-based solutions rely on large datasets with limited generalization. To address these challenges, we present Pareto-Optimal Visual Navigation, a lightweight image-space framework that combines data-driven semantics, Pareto-optimal decision-making, and visual servoing for real-time navigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space Pushp, Durgakant Chen, Weizhe Chen, Zheng Luo, Chaomin Gregory, Jason M. Liu, Lantao Robotics Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings, map-based approaches demand heavy mapping effort, and learning-based solutions rely on large datasets with limited generalization. To address these challenges, we present Pareto-Optimal Visual Navigation, a lightweight image-space framework that combines data-driven semantics, Pareto-optimal decision-making, and visual servoing for real-time navigation. |
| title | Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.07750 |