GoKawiilRobostral 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 simulation-trained 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, paving the way for unified embodied AI in robotics.
Today we're introducing Robostral Navigate, our first model built for embodied navigation. It's an 8B model that takes RGB images and a plain-language instruction and moves a robot through an environment:
“Leave the lobby, walk through the corridor, enter the supply room, and stop to face the second shelf.”
To perform such tasks, other models often employ depth sensors, LiDAR, or several cameras working together. Robostral Navigate uses only one ordinary RGB camera and no depth sensors, yet still achieves 76.6% on R2R-CE (Room-to-Room in Continuous Environments) validation unseen, the benchmark for following instructions in environments held out of training. Consequently, it beats the best single-camera approach by 9.7 points and the best system using depth or multiple cameras by 4.5 points, despite using neither.
Navigation
Our model is designed for robotic navigation, enabling robots to autonomously navigate complex environments, including offices, residential and commercial buildings, and outdoor settings.
Robostral Navigate running fully autonomously in one long-horizon instruction route through a working office.
This technology unlocks numerous applications across manufacturing, delivery, logistics, and hospitality, making it one of the most in-demand capabilities for our customers today. Give Robostral Navigate one instruction and it completes the entire task on its own, moving through a live space full of people and obstacles it was never shown, capable of adapting to any setting.
Highlights
State-of-the-art performance on R2R-CE
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