Robotics: Science and Systems XXI

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

Arthur Zhang, Harshit Sikchi, Joydeep Biswas, Amy Zhang

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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Bibtex:

  
@INPROCEEDINGS{ZhangA-RSS-25, 
    AUTHOR    = {Arthur Zhang AND Harshit Sikchi AND Joydeep Biswas AND Amy Zhang}, 
    TITLE     = {{CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2025}, 
    ADDRESS   = {LosAngeles, CA, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2025.XXI.136} 
}