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arXiv · 2509.11791

Synthetic vs. Real Training Data for Visual Navigation

Lauri Suomela·Sasanka Kuruppu Arachchige·German F. Torres·Harry Edelman·Joni-Kristian Kämäräinen
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Abstract

This paper investigates how the performance of visual navigation policies trained in simulation compares to policies trained with real-world data. Performance degradation of simulator-trained policies is often significant when they are evaluated in the real world. However, despite this well-known sim-to-real gap, we demonstrate that simulator-trained policies can match the performance of their real-world-trained counterparts. Central to our approach is a navigation policy architecture that bridges the sim-to-real appearance gap by leveraging pretrained visual representations and runs real-time on robot hardware. Evaluations on a wheeled mobile robot show that the proposed policy, when trained in simulation, outperforms its real-world-trained version by 31 and the prior state-of-the-art methods by 50 points in navigation success rate. Policy generalization is verified by deploying the same model onboard a drone. Our results highlight the importance of diverse image encoder pretraining for sim-to-real generalization, and identify on-policy learning as a key advantage of simulated training over training with real data. Code, model checkpoints and multimedia materials are available at https://lasuomela.github.io/faint/

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Lauri Suomela, Sasanka Kuruppu Arachchige, German F. Torres, Harry Edelman, Joni-Kristian Kämäräinen. 2026-02-24. Synthetic vs. Real Training Data for Visual Navigation. https://arxiv.org/abs/2509.11791

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