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
ऑटोमोबाइल मोबाइल रोबोट कोड जो घने भीड़ों और परेशान साइटों में संचालित करते हैं, आसपास के खतरों के प्रति प्रतिक्रिया करने के लिए $ 360\circ$ संवेदना की आवश्यकता होती है. जबकि पैनोरैमिक LiDAR पूर्ण-surround कवरेज प्रदान करता है, रीमेडिंग सीखने (आरएल) के भीतर सीखने के प्रतिनिधित्वों को छोटे पुरस्कारों के तहत नमूना-अक्षम है, और मौजूदा 3D प्री-ट्रेनिंग पॉइंट-क्लाउड एन्कोडर बड़े पैमाने पर समायोज्य आरएल सिम्युलेशन के लिए बहुत मेमोरी-बारीक होते हैं. हम RangeNav, एक कॉम्पैक्ट (183k पैरामीटर) LiDAR रेंज-इ
Machine translation (Meta M2M100); the English original follows.
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. Project website: https://rangenav.github.io/.
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Topics: Lidar, Computer science, Artificial intelligence, Computer vision, Encoder, Ranging
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