Robostral Navigate is an 8B model that enables robots to autonomously navigate complex environments using only a single RGB camera, achieving 76.6% success on unseen R2R-CE benchmarks—outperforming multi-sensor approaches while being more efficient. Built entirely in-house with simulated data and token-efficient techniques, it generalizes across robot types and adapts to real-world obstacles unseen during training. The model combines pointing-based navigation with reinforcement learning for continuous improvement. It takes RGB images and a plain-language instruction and moves a robot through an environment. Robostral Navigate uses only one ordinary RGB camera and no depth sensors, yet still achieves 76.6% on R2R-CE validation unseen. It beats the best single-camera approach by 9.7 points and the best system using depth or multiple cameras by 4.5 points. The model is designed for robotic navigation in offices, residential and commercial buildings, and outdoor settings. It achieves a 79.4% Success Rate on validation seen and a 76.6% Success Rate on validation unseen. It runs on wheeled, legged, and flying robots, and generalizes across robot sizes. Navigation is done via pointing, where the model infers the image coordinates of the target location in the robot's current camera view, together with the desired orientation upon arrival.