Robostral Navigate: single-camera AI navigation | Mistral AI

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.

State-of-the-art performance on R2R-CE

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

A key ingredient of Robostral Navigate is an efficient training algorithm based on prefix-caching. Using a tree-based attention-masking strategy, our method compresses an entire episode into a single sequence, enabling training on all time steps in a single forward pass while preventing information leakage between time steps.

Compared to training with one sample per time step, our approach reduces the number of training tokens by 22× while preserving all of the learning signals. In practice, this method transforms training runs that would take months into runs that complete in days.