{"ID":2862449,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.25687","arxiv_id":"2509.25687","title":"OmniNav: A Unified Framework for Prospective Exploration and Visual-Language Navigation","abstract":"Embodied navigation presents a core challenge for intelligent robots, requiring the comprehension of visual environments, natural language instructions, and autonomous exploration. Existing models often fall short in offering a unified solution across diverse navigation paradigms, resulting in low success rates and limited generalization. We introduce OmniNav, a unified framework addressing instruct-goal, object-goal, point-goal navigation, and frontier-based exploration within a single architecture. Our approach features a lightweight, low-latency policy that accurately predicts continuous-space waypoints (coordinates and orientations). This policy surpasses action-chunk methods in precision and supports real-world deployment at control frequencies up to 5 Hz. Architecturally, OmniNav employs a fast-slow system design: a fast module generates waypoints using short-horizon visual context and subtasks, while a slow module performs deliberative planning with long-horizon observations and candidate frontiers to select subsequent subgoals and subtasks. This collaboration enhances path efficiency and maintains trajectory coherence, particularly in exploration and memory-intensive scenarios. Crucially, we identify that the primary bottleneck isn't merely navigation policy learning, but a robust understanding of general instructions and objects. To boost generalization, OmniNav integrates large-scale, general-purpose training datasets, including those for image captioning and visual recognition, into a joint multi-task regimen. This significantly improves success rates and robustness. Extensive experiments confirm OmniNav's state-of-the-art performance across various navigation benchmarks, with real-world deployment further validating its efficacy. OmniNav provides practical insights for embodied navigation, charting a scalable path towards versatile, highly generalizable robotic intelligence.","short_abstract":"Embodied navigation presents a core challenge for intelligent robots, requiring the comprehension of visual environments, natural language instructions, and autonomous exploration. Existing models often fall short in offering a unified solution across diverse navigation paradigms, resulting in low success rates and lim...","url_abs":"https://arxiv.org/abs/2509.25687","url_pdf":"https://arxiv.org/pdf/2509.25687v3","authors":"[\"Xinda Xue\",\"Junjun Hu\",\"Minghua Luo\",\"Shichao Xie\",\"Jintao Chen\",\"Zixun Xie\",\"Kuichen Quan\",\"Wei Guo\",\"Mu Xu\",\"Zedong Chu\"]","published":"2025-09-30T02:44:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"LoRA\"]","has_code":false}