This looks to not be an openly available model, but I think if it were, availability of an easy single-camera navigation setup could allow for a lot of cool hobbyist projects.
It's implied, and I'm hoping it's true, that this is a map-less navigation. Which is impressive. This kind of task is much easier if you have a pre-captured map of the environment, but if they are doing this without a map it's great. Historically you were always faced with "The Kidnapped Robot" problem where robots that didn't know where they were couldn't navigate even a little bit. Here the robot appears to be able to follow directions as long as they are interpretable from its current vision (or via dead reckoning).
If you're wondering what prevents or mitigates AI hallucinations on the AI layer from replicating or acting out on the physical layer look up QNX. They manage the deterministic reasonin gof robotics. You know them better as Blackberry.
Producing specific niche models for 100 year old industries that have mountains of data and warehouses full of folders will be the european take on AI.
It may come late but it‘ll be safe and reliable. It also requires a lot of OCR.
The Niche model story is still fairly week. Evidence points to general models being equally capable to niche models at a more attractive capex (risk is spread across multiple verticals rather than concentrated in a single model capability)
The General Models' business-model is also looking more weak every iteration.
Costs of simple tasks grow extensively: OCR with "Mistral OCR" at $4 per 1000 pages vs OCR with Opus 4.8 at sometimes¹ $1 per "page".
Or just the immense costs when burning tokens in an unoptimized agentic coding environment costing tens of dollars for a few simple classes or functions versus a highly optimized "autocomplete" model costing under $10 for thousands of such classes and functions.
Or the, over ten dollars worth of tokens when some "agent" using a general model, tries to churn trough the simple task of reading a web-page, extracting dates from it and (for some unknown reason, suddenly) refuses to use the google-cal tool/mcp but writes -then debugs- some python code to interact with the google-calendar-API. Where some dedicated code, using only a simple LLM for the "extracting dates" part but all the rest as "old fashioned" code.
The "general models" currently only become smarter by growing bigger and having larger context windows - by becoming exponentially more expensive to train and to run and to interact with. Whereas "Niche" models can do the things that "normal code" cannot do. Can fill in gaps that traditionally are hard or impossible with normal software, without replacing that entire "normal software".
One example: I am not interested in "chatting with my calendar". I'm interested in a calendar because it is a well known view (UI) of my planning and tasks, but I see a lot of opportunities where AI can improve my working with this calendar. I may be interested in a smarter screen when I hit "+ Add event"; one that has knowledge of my previous events and patterns (some RAG vector db maybe). One that maybe has access to content I just copied, or read (though: privacy?) or can open my camera to let me shoot a pic of something that has the event info on it.
In such a set-up, Niche LLMs perform dedicated tasks: determine patterns (he always books a Yoga class on wednesday or thursday, two days in advance, so lets suggest a yoga class), determine existing content (event is planned 100Km from his home, so lets suggest the commute based on previous commutes like this). Or an OCR model. Or an autocomplete model. Relatively simple, niche models, called from within software to aid me when "calendaring". Not replace the entire calendar with some chat.
It seems like a stronger story for robotics, since smaller models can always react to the environment faster than large models for a given level of hardware resources. Also because robots that keep their models local for latency or robustness reasons aren't going to be carrying many kilowatts of inference capacity.
For a claim such as state of the art, or claims such as "great at any task" needs something of more substance. I've seen maze-solving robot competitions which can zoom around in seconds. The sped up video in the first part, and the "obstacle avoidance" are too slow for me to believe this is state of the art.
While impressive at 8B, what would the expectation be in real life, that it's run remotely or autonomously with a strapped on GPU and battery?
I've used that example as a contrast of what I've seen before. If you can point me at comparable efforts, in the same category as what Mistral is doing, I'd be interested in having a comparative look.
All I can think of are robot dogs, Tesla bots, and whatever flavor of the month Japanese robots show up at trade shows.
Ok, this is really cool. The fact that the robot can use pointing to decide where to go is a great design decision, and robotics really is the next frontier. Definitely cheering on Mistral here!
It may come late but it‘ll be safe and reliable. It also requires a lot of OCR.
Costs of simple tasks grow extensively: OCR with "Mistral OCR" at $4 per 1000 pages vs OCR with Opus 4.8 at sometimes¹ $1 per "page".
Or just the immense costs when burning tokens in an unoptimized agentic coding environment costing tens of dollars for a few simple classes or functions versus a highly optimized "autocomplete" model costing under $10 for thousands of such classes and functions.
Or the, over ten dollars worth of tokens when some "agent" using a general model, tries to churn trough the simple task of reading a web-page, extracting dates from it and (for some unknown reason, suddenly) refuses to use the google-cal tool/mcp but writes -then debugs- some python code to interact with the google-calendar-API. Where some dedicated code, using only a simple LLM for the "extracting dates" part but all the rest as "old fashioned" code.
The "general models" currently only become smarter by growing bigger and having larger context windows - by becoming exponentially more expensive to train and to run and to interact with. Whereas "Niche" models can do the things that "normal code" cannot do. Can fill in gaps that traditionally are hard or impossible with normal software, without replacing that entire "normal software".
One example: I am not interested in "chatting with my calendar". I'm interested in a calendar because it is a well known view (UI) of my planning and tasks, but I see a lot of opportunities where AI can improve my working with this calendar. I may be interested in a smarter screen when I hit "+ Add event"; one that has knowledge of my previous events and patterns (some RAG vector db maybe). One that maybe has access to content I just copied, or read (though: privacy?) or can open my camera to let me shoot a pic of something that has the event info on it. In such a set-up, Niche LLMs perform dedicated tasks: determine patterns (he always books a Yoga class on wednesday or thursday, two days in advance, so lets suggest a yoga class), determine existing content (event is planned 100Km from his home, so lets suggest the commute based on previous commutes like this). Or an OCR model. Or an autocomplete model. Relatively simple, niche models, called from within software to aid me when "calendaring". Not replace the entire calendar with some chat.
Unless you are in military robotics or automotive of course :)
I would like to know what it did the other 23.4% of the time!
While impressive at 8B, what would the expectation be in real life, that it's run remotely or autonomously with a strapped on GPU and battery?
All I can think of are robot dogs, Tesla bots, and whatever flavor of the month Japanese robots show up at trade shows.
I imagine the EU would block any attempted takeover of Mistral given recent Anthropic and US govt actions.
But I'm scared for when those home helpers get drafted to fight in wars, either for or against me...
SOTA 80% means a practically useless robot. What are they really imagining their ICP to be here?