TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals

• URL: http://arxiv.org/abs/2509.08699v1
• Date: Wed, 10 Sep 2025 15:43:32 GMT
• Title: TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals
• Authors: Stefan Podgorski, Sourav Garg, Mehdi Hosseinzadeh, Lachlan Mares, Feras Dayoub, Ian Reid,
• Abstract summary: We present a novel RGB-only, object-level topometric navigation pipeline that enables zero-shot, long-horizon robot navigation.<n>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.<n>We demonstrate the effectiveness of our method in both simulated environments and real-world tests, highlighting its robustness and deployability.
• Score: 10.69725316052444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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