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About this paper
Samuel Foo Enze, Manthan Patel, Fan Yang, Linus Kramer, Pascal Roth, Marco Hutter, Cesar Cadena
RangeNav trains a compact panoramic LiDAR encoder on synthetic data, then freezes it for recurrent reinforcement-learning navigation. The encoder learns range reconstruction, object semantics, and bird’s-eye-view traversability, combining geometric and task-related supervision. Across four procedural and four held-out environments, its full objective reaches mean success rates of 95.46% and 73.17%, respectively; physical-robot evidence is qualitative zero-shot rollout.
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