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[2509.11791] Synthetic vs. Real Training Data for Visual Navigation
Summary
This paper examines the effectiveness of visual navigation policies trained in simulation versus those trained with real-world data, highlighting significant performance improvements in simulated models.
Why It Matters
Understanding the sim-to-real gap in visual navigation is crucial for advancing robotics and machine learning applications. This research demonstrates that simulated training can yield superior performance, which could lead to more efficient training methods and better real-world applications.
Key Takeaways
- Simulator-trained policies can outperform real-world-trained counterparts by significant margins.
- Diverse image encoder pretraining is vital for effective sim-to-real generalization.
- On-policy learning offers advantages in simulated training that enhance performance.
Computer Science > Robotics arXiv:2509.11791 (cs) [Submitted on 15 Sep 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Synthetic vs. Real Training Data for Visual Navigation Authors:Lauri Suomela, Sasanka Kuruppu Arachchige, German F. Torres, Harry Edelman, Joni-Kristian Kämäräinen View a PDF of the paper titled Synthetic vs. Real Training Data for Visual Navigation, by Lauri Suomela and 4 other authors View PDF HTML (experimental) 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 ide...