CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance

• URL: http://arxiv.org/abs/2503.03921v2
• Date: Thu, 26 Jun 2025 06:42:04 GMT
• Title: CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
• Authors: Arthur Zhang, Harshit Sikchi, Amy Zhang, Joydeep Biswas,
• Abstract summary: CREStE is a scalable learning-based mapless navigation framework.<n>It addresses the open-world generalization and robustness challenges of outdoor urban navigation.<n>We evaluate CREStE on the task of kilometer-scale mapless navigation in a variety of city, offroad, and residential environments.
• Score: 13.922655150502365
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to achieving this is learning perceptual representations that generalize to open-set factors (e.g. novel semantic classes, terrains, dynamic entities) and inferring expert-aligned navigation costs from limited demonstrations. CREStE addresses both these issues, introducing 1) a visual foundation model (VFM) distillation objective for learning open-set structured bird's-eye-view perceptual representations, and 2) counterfactual inverse reinforcement learning (IRL), a novel active learning formulation that uses counterfactual trajectory demonstrations to reason about the most important cues when inferring navigation costs. We evaluate CREStE on the task of kilometer-scale mapless navigation in a variety of city, offroad, and residential environments and find that it outperforms all state-of-the-art approaches with 70% fewer human interventions, including a 2-kilometer mission in an unseen environment with just 1 intervention; showcasing its robustness and effectiveness for long-horizon mapless navigation. Videos and additional materials can be found on the project page: https://amrl.cs.utexas.edu/creste

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