Mistral's Robostral Navigate: a state of the art robotics navigation model

Thinking

Summary

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, 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.

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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-  **79.4% Success Rate** on validation seen

- 76.6% Success Rate on validation unseen

- Operates from a single RGB camera, with no LiDAR or depth sensors

- 8B model, built in-house and trained entirely in simulation

- Runs on wheeled, legged, and flying robots, and generalizes across robot sizes

- Robust to differences in camera intrinsics

- Token-efficient training via prefix-caching

**Navigation via pointing**

Given a task and a history of observations, Robostral Navigate predicts where the robot should move next via pointing : it infers the image coordinates of the target location in the robot's current camera view, together with the desired orientation upon arrival. Unlike commands relying on metric displacements, pointing makes the policy naturally robust to changes in camera intrinsics and world scale.

However, this method cannot handle cases where the target location lies outside the current field of view. When pointing does not apply, the model falls back to displacements in the robot's local coordinate frame, such as:

"Move 2 meters forward, 1.5 meters to the left, and turn 25 degrees left."

**Built from the ground up**

Robostral Navigate is built entirely in-house and does not rely on existing open-source VLMs.