TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals
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arXiv
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| Format: | Preprint |
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2025
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| author | Podgorski, Stefan Garg, Sourav Hosseinzadeh, Mehdi Mares, Lachlan Dayoub, Feras Reid, Ian |
| author_facet | Podgorski, Stefan Garg, Sourav Hosseinzadeh, Mehdi Mares, Lachlan Dayoub, Feras Reid, Ian |
| contents | Visual navigation in robotics traditionally relies on globally-consistent 3D maps or learned controllers, which can be computationally expensive and difficult to generalize across diverse environments. In this work, we present a novel RGB-only, object-level topometric navigation pipeline that enables zero-shot, long-horizon robot navigation without requiring 3D maps or pre-trained controllers. Our approach integrates global topological path planning with local metric trajectory control, allowing the robot to navigate towards object-level sub-goals while avoiding obstacles. We address key limitations of previous methods by continuously predicting local trajectory using monocular depth and traversability estimation, and incorporating an auto-switching mechanism that falls back to a baseline controller when necessary. The system operates using foundational models, ensuring open-set applicability without the need for domain-specific fine-tuning. We demonstrate the effectiveness of our method in both simulated environments and real-world tests, highlighting its robustness and deployability. Our approach outperforms existing state-of-the-art methods, offering a more adaptable and effective solution for visual navigation in open-set environments. The source code is made publicly available: https://github.com/podgorki/TANGO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals Podgorski, Stefan Garg, Sourav Hosseinzadeh, Mehdi Mares, Lachlan Dayoub, Feras Reid, Ian Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Systems and Control Visual navigation in robotics traditionally relies on globally-consistent 3D maps or learned controllers, which can be computationally expensive and difficult to generalize across diverse environments. In this work, we present a novel RGB-only, object-level topometric navigation pipeline that enables zero-shot, long-horizon robot navigation without requiring 3D maps or pre-trained controllers. Our approach integrates global topological path planning with local metric trajectory control, allowing the robot to navigate towards object-level sub-goals while avoiding obstacles. We address key limitations of previous methods by continuously predicting local trajectory using monocular depth and traversability estimation, and incorporating an auto-switching mechanism that falls back to a baseline controller when necessary. The system operates using foundational models, ensuring open-set applicability without the need for domain-specific fine-tuning. We demonstrate the effectiveness of our method in both simulated environments and real-world tests, highlighting its robustness and deployability. Our approach outperforms existing state-of-the-art methods, offering a more adaptable and effective solution for visual navigation in open-set environments. The source code is made publicly available: https://github.com/podgorki/TANGO. |
| title | TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2509.08699 |