{"ID":2843088,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07750","arxiv_id":"2511.07750","title":"Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space","abstract":"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.","short_abstract":"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 addre...","url_abs":"https://arxiv.org/abs/2511.07750","url_pdf":"https://arxiv.org/pdf/2511.07750v1","authors":"[\"Durgakant Pushp\",\"Weizhe Chen\",\"Zheng Chen\",\"Chaomin Luo\",\"Jason M. Gregory\",\"Lantao Liu\"]","published":"2025-11-11T02:01:54Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}