Newest papers

RangeNav: Task-Aligned LiDAR Range-Image Pretraining for Robot Navigation

Samuel Foo Enze, Manthan Patel, Fan Yang, Linus Kramer, Pascal Roth, Marco Hutter, César Cadena · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29845 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

密集した群衆や混乱した場所で動作する自動モバイルロボットコードは、周囲の危険に反応するために360ドルの認識を必要とします。パノラマリックLIDARは完全なサーバーカバーを提供する一方、強化学習(RL)内の学習表示は、わずかな報酬の下でサンプル非効率であり、既存の3Dプレトレインドポイントクラウドエンコーダーは、大量に並列的なRLシミュレーションのためのメモリ重量である。我々はRangeNav、コンパクトな(183kパラメータ)LIDAR範囲イメージエンコーダーを、完全に合成データにプレトレインしています。RangeNavは、非イゾトロピックコボリューションと

Machine translation (Meta M2M100); the English original follows.

Abstract

Autonomous mobile robots operating in dense crowds and cluttered sites require $360^\circ$ perception to react to surrounding hazards. While panoramic LiDAR offers full-surround coverage, learning representations within reinforcement learning (RL) is sample-inefficient under sparse rewards, and existing 3D pretrained point-cloud encoders are too memory-heavy for massively parallel RL simulation. We present RangeNav, a compact (183k parameters) LiDAR range-image encoder pretrained entirely on synthetic data. RangeNav uses anisotropic convolutions and circular padding to handle the extreme aspect ratio and azimuthal seam of range images. We pretrain the network using a navigation-aligned objective combining range reconstruction, semantic segmentation, and bird's-eye-view (BEV) prediction, then freeze it for policy learning. In simulation, adding semantic and BEV supervision increases success rates over a reconstruction-only baseline by an average of 32.03 and 23.73 percentage points (pp) in procedurally generated training and out-of-distribution realistic test environments, respectively. Zero-shot real-world deployments on a Unitree B2-W quadruped demonstrate robust navigation through dense crowds, low-light settings, and construction zones where limited-field-of-view depth policies struggle. Code and models will be open-sourced.

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Topics: Lidar, Computer science, Artificial intelligence, Computer vision, Encoder, Ranging

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange