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Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments

arXiv:2605.28442v2 Announce Type: replace Abstract: Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation behavior toward safe and efficient trajectories. Existing approaches either rely on handcrafted proprioceptive traversability scores, limiting robot-agnosticism, or cluster prior data, preventing online learning. Moreover, many continual learning methods incur substantial memory and...

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