Announcing Robostral Navigate, our first model for embodied navigation: an 8B robotics navigation model that guides robots to autonomously perform tasks specified with natural language. Single RGB camera. State-of-the-art on R2R-CE.

No LiDAR. No depth sensors. No camera rig. Where leading systems lean on depth or multiple cameras, Robostral Navigate works from one ordinary RGB camera, and still comes out ahead.

76.6% success on R2R-CE validation unseen (79.4% on validation seen), the benchmark for following instructions in previously unseen environments. It beats the best single-camera approach by 9.7 points while using far less sensing.

Trained entirely in simulation: ~400,000 trajectories across 6,000 scenes. A prefix-caching recipe cuts training tokens by 22×, turning months-long runs into days. Online RL (CISPO) pushes success rates higher still.

It runs on wheeled, legged, and flying robots and generalizes across sizes, unlocking delivery, logistics, manufacturing, and hospitality. Read more:

@fouriergalois This is fun and all but why would you spread yourself thin like that?

single rgb camera doing the whole navigation job is the impressive part here

@Scobleizer we needed le chaton fat, but we got metal guy 😭

We can help robostral see around corners without adding any more hardware or cost.

Single RGB camera robot navigation is wild, my vacuum still loses to chair legs.

I thought Daft Punk split up few years ago! 😍

i swear Mistral AI is the company that makes Europe relevant and proud. Keep at it!

say what you have about Mistral, but their model names are top notch. Devstral, Magistral, Pixtral , Codestral and now Robstral!

this is so cool, most robotics demos require sensor arrays that costs more than the robot, this is changing the game

Sick

super sick

Cool

8B and a single RGB camera is the headline: no depth sensor, no lidar, just learned priors. That puts navigation in reach of any robot with a webcam. Curious how R2R-CE transfers to messy real homes versus staged evals.

Mistral AI, does ‘Robostral Navigate’ have training data for Rov’s? If not i might know someone that can get you plenty. Getting ready to have Hermes Agent interface with this Trident with your model for total control. Let me know, DM’s are open. @Teknium @NousResearch @mr_r0b0t

It's exciting to see Mistral entering the Physical AI race. Starting from the ground up by solving navigation, rather than jumping straight to flashy manipulation tasks, might actually be the more important approach.

Embodied navigation is where agent reliability gets very concrete. For robots, I’d watch not only task success, but recovery after a bad step, latency variance, and the fallback path when natural-language intent is underspecified. Benchmarks matter; operational behavior matters more.

I wonder how much data Roomba collected to help build this logic. 😂

@droneforge will you guys be testing this model out, flying robots are listed as compatible.

Single-camera NL navigation is a clean product surface. The hard part after the paper: recovery evals under lighting shift, occlusion, and wrong-turn recovery — demos rarely show the failure loop. #Robotics #PhysicalAI

@Presidentlin

#zeitgeist

wait what

It's time to create your own comma

Impressive milestone! The ability to leverage natural language for navigation opens up new avenues for human-robot interaction. How do you see this impacting real-world applications in complex environments?

@GuillaumeLample 👏👏👏

Interesting because embodied navigation turns model reliability into physical-world reliability. For production robotics, I’d watch not just benchmark score, but route-failure recovery, p95 action latency, camera edge cases, and explicit stop/escalation rules.

Robostral Navigate听起来很有前景，但关键在于实际部署时的可靠性和稳定性。在复杂多变的真实环境中，模型能否保持高效导航是需要重点关注的问题。

Natural language control with a single RGB camera is impressive. Excited to see where this goes.

single camera gets the headline but 8B is the sleeper. small enough to actually run on the robot. a nav loop can't wait on a cloud round trip, one tail-latency spike and it's already into the chair leg. on device isn't the cheap path here, it's the only one that closes the loop in time.

waiting for the FAT version

Ok google let's try that hand