Gemini 3 is the only model I've found that can reason spatially. The results here are accurate to my experiments with putting LLM NPCs in simulated worlds.
I was surprised that most VLLMs cannot reliably tell if a character is facing left or right, they will confidently lie no matter what you do (even gemini 3 cannot do it reliably). I guess it's just not in the training data.
That said Qwen3VL models are smaller/faster and better "spatially grounded" in pixel space, because pixel coordinates are encoded in the tokens. So you can use them for detecting things in the scene, and where they are (which you can project to 3d space if you are running a sim). But they are not good reasoning models so don't ask them to think.
That means the best pipeline I've found at the moment is to tack a dumb detection prepass on before your action reasoning. This basically turns 3d sims into 1d text sims operating on labels -- which is something that LLMs are good at.
I suspect the latency on Gemini 3 makes it non-viable for a real-time control loop though. Even if the reasoning works, the input token costs would destroy the unit economics pretty quickly. I'd be worried about relying on that kind of API overhead for the critical path.
Neuro-sama, the V-Tuber/AI actually does a decent job of it. Vedal seems to have cooked and figured out how to make an LLM move reasonably well in VRChat.
Not perfectly, there's a lot abuse of gravity or the lack thereof, but yeah. Neuro has also piloted a Robot Dog in the past.
This is what VLA models are for. They would work much better. Would need a bit of fine tuning but probably not much. Lots of literature out there on using VLAs to control drones.
I don't understand. Surely training an LSTM with sensor input is more practical and reasonable way than trying to get a text generator to speak commands to a drone.
The fact that a language model can „reason“ (in the LLM-slang meaning of the term) about 3D space is an interesting property.
If you give a text description of a scene and ask a robot to perform a peg in hole task, modern models are able to solve them fairly easily based on movement primitives. I implemented this on a UR robot arm back in 2023
The next logical step is, instead of having the model output text (code representing movement primitives), outputting tokens in action space. This is what models like pi0 are doing.
I mean semantically language evolved as an interpretation for the material world, so assuming that you can describe a problem in language, and considering that there exists a solution to said problem that is describable in language, then I'm sure a big enough LLM could do it... but you can also calculate highly detailed orbital maps with epicycles if you just keep adding more... you just don't because it's a waste of time and there's a simpler way.
The latter part is interesting. I'm not sure how the performance of one of those would be once they are working well, but my naive gut feeling is that splitting the language part and the driving part into two delegates is cleaner, safer, faster and more predictable.
note that the control systems you were talking about before (i.e. PID) would probably take hold pretty directly in a tiny network, and exactly because of that limitation, be far less likely to contain 'hallucinations'. object avoidance and path planning are likely similar.
since this is a limited and continuous domain, its a far better one for neural training than natural language. I guess this notion that a language model should be used for 3d motion control is a real indicator about the level of thought going into some of these applications.
On the discussion of the right or wrong tool, I find it possible that the ability to reason towards a goal is more valuable in the long run than an intrinsic ability to achieve the same result. Or maybe a mix of both is the ideal.
This is neat! It's a bit amusing in that I worked on a somewhat similar project for my phd thesis almost 10 years ago, although in that case we got it working on a real drone (heavily customized, based on DJI matrice) in the field, with only onboard compute. Back then it was just a fairly lightweight CNN for the perception, not that we could've gotten much more out of the jetson TX2.
Why would you want an LLM to fly a drone? Seems like the wrong tool for the job -- it's like saying "Only one power drill can pound roofing nails". Maybe that's true, but just get a hammer
There are almost endless reasons why. It's like asking why would you want a self-driving car. Having a drone to transport things would be amazing, or to patrol an area. LLMs can be helpful with object identification, reacting to different events, and taking commands from users.
The first thought I had was those security guard robots that are popping up all over the place. if they were drones instead, and LLM talked to people asking them to do/not-do things, that would be an improvement.
Or an waiter drone, that takes your order in a restaurant, flies to the kitchen, picks up a sealed and secured food container, flies it back to the table, opens it, and leaves. It will monitor for gestures and voice commands to respond to diners and get their feedback, abuse, take the food back if it isn't satisfactory,etc...
This is the type of stuff we used to see in futuristic movies. It's almost possible now. glad to see this kind of tinkering.
You could have a program, not LLM-based but could be ANN, for flying and an LLM for overseeing; the LLM could give the program instructions to the pilot program as a (x,y,z) directions. I mean currently autopilots are typically not LLMs, right?
You describe why it would be useful to have an LLM in a drone to interact with it but do not explain why it is the very same LLM that should be doing the flying.
I'm not OP, I don't know what specific roles the LLM should be using, but LLMs are great with object recognition, and using both text (street signs,notices,etc..) and visual cues to predict the correct response. The actual motor control i'm sure needs no LLMs, but the decision making could use any number of solutions, I agree that an LLM-only solution sounds bad, but I didn't do the testing and comparison to be confident in that assessment.
An LLM that can't understand the environment properly can't properly reason about which command to give in response to a user's request. Even if the LLM is a very inefficient way to pilot the thing, being able to pilot means the LLM has the reasoning abilities required to also translate a user's request into commands that make sense for the more efficient, lower-level piloting subsystem.
That’s a pretty boring point for what looks like a fun project. Happy to see this project and know I am not the only one thinking about these kinds of applications.
We don't need a lot of things, but new tech should also address what people want, not just needs. I don't know how to pilot drones, nor do I care to learn how to, but I want to do things with drones, does that qualify as a need? Tech is there to do things for us we're too lazy to do.
You're considering "talking to" a separate thing, I consider it the same as reading street signs or using object recognition. My voice or text input is just one type of input. Can other ML solutions or algorithms detect a tree (same as me telling it there is a tree,yaw to the right), yes, can LLMs detect a tree and determine what course of action to take? also true. Which is better? I don't know, but I won't be quick to dismiss anyone attempting to use LLMs.
Definitely maybe - but then we are discussing (2), i.e. "what is the right technical solution to solve (1)".
Your previous comment was arguing that (1) is great (which no one denies in this thread, and it is a different discussion about what products are desirable rather than how to build said product) in an answer to someone arguing (2).
I don't think you understand what an "LLM" is. They're text generators. We've had autopilot since the 1930s that relies on measurable things... like PID loops, direct sensor input. You don't need the "language model" part to run an autopilot, that's just silly.
You see to be talking past him and ignoring what they are actually saying.
LLMs are a higher level construct than PID loops. With things like autopilot I can give the controller a command like 'Go from A to B', and chain constructs like this to accomplish a task.
With an LLM I can give the drone/LLM system complex command that I'd never be able to encode to a controller alone. "Fly a grid over my neighborhood, document the location of and take pictures of every flower garden".
And if an LLM is just a 'text generator' then it's a pretty damned spectacular one as it can take free formed input and turn it into a set of useful commands.
They are text generators, and yes they are pretty good, but that really is all they are, they don't actually learn, they don't actually think. Every "intelligence" feature by every major AI company relies on semantic trickery and managing context windows. It even says it right on the tin; Large LANGUAGE Model.
Let me put it this way: What OP built is an airplane in which a pilot doesn't have a control stick, but they have a keyboard, and they type commands into the airplane to run it. It's a silly unnecessary step to involve language.
Now what you're describing is a language problem, which is orchestration, and that is more suited to an LLM.
Give the LLM agent write acces to a text file to take notes and it can actually learn. Not really realiable, but some seem to get useful results. They ain't just text generators anymore.
(but I agree that it does not seem the smartest way to control a plane with a keyboard)
My confusion maybe? Is this simulator just flying point a to b? Seems like it’s handling collisions while trying to locate the targets and identify them. That seems quite a bit more complex than what you are describing has been solved since the 1930s.
LLMs can do chat-completion, they don't do only chat completion. There are LLMs for image generation, voice generation, video generation and possibly more. The camera of a drone inputs images for the LLM, then it determines what action take based on that. Similar to if you asked ChatGPT "there is a tree in this picture, if you were operating a drone, what action would you take to avoid collision", except the "there is a tree" part is done by the LLMs image recognition, and the sys prompt is "recognize objects and avoid collision", of course I'm simplifying it a lot but it is essentially generating navigational directions under a visual context using image recognition.
Yes it can be, and often is. Advanced voice mode in chatGPT and the voice mode in Gemini are LLMs. So is the image gen in both chatGPT and Gemini (Nano Banana).
"You don't need the "language model" part to run an autopilot, that's just silly."
I think most of us understood that reproducing what existing autopilot can do was not the goal. My inexpensive DJI quadcopter has an impressive abilities in this area as well. But, I cannot give it a mission in natural language and expect it to execute it. Not even close.
SOTA typically refers to achieving the best performance, not using the trendiest thing regardless of performance. There is some subtlety here. At some point an LLM might give the best performance in this task, but that day is not today, so an LLM is not SOTA, just trendy. It's kinda like rewriting something in Rust and calling it SOTA because that's the trend right now. Hope that makes sense.
>Using an LLM is the SOTA way to turn plain text instructions into embodied world behavior.
>SOTA typically refers to achieving the best performance
Multimodal Transformers are the best way to turn plain text instructions to embodied world behavior. Nothing to do with being 'trendy'. A Vision Language Action model would probably have done much better but really the only difference between that and the models trialed above is training data. Same technology.
I don’t think trendy is really the right word and maybe it’s not state of the art but a lot of us in the industry are seeing emerging capabilities that might make it SOTA. Hope that makes sense.
LLMs are indeed the definition of trendy (I've found using Google Trends to dive in is a good entry point to get a broad sense of whether something is "trendy")! Basically the right way to think about it is that something can be promising, and demonstrate emerging capabilities, but but those things don't make something SOTA, nor do they make it trendy. They can be related though (I expect everything SOTA was once promising and emerging, but not everything promising or emerging became SOTA). It's a subtlety that isn't super easy to grasp, but (and here is one area I think an LLM can show promise) an LLM like ChatGPT can help unpick the distinctions here. Still, it's slightly nuanced and I understand the confusion.
I think the point may have flown over your head. I am suggesting you are being dismissive with a distinct lack of thought on your reply. Like said I don’t think state of the art is the right way to describe it but I think trendy is equally wrong from the other side of the spectrum. Models that can deal with vision have some really interesting use cases and ones that can be valuable, in a lot of ways I would say state of the art could describe it but I know to folks that are hopelessly negative, it’s a hard reach so I was trying to balance it for you. Hope that makes sense.
It's a great feature to tell my drone to do a task in English. Like "a child is lost in the woods around here. Fly a search pattern to find her" or "film a cool panorama of this property. Be sure to get shots of the water feature by the pool."
While LLMs are bad at flying, better navigation models likely can't be prompted in natural language yet.
What you're describing is still ultimately the "view" layer of a larger autopilot system, that's not what OP is doing. He's getting the text generator to drive the drone. An LLM can handle parsing input, but the wayfinding and driving would (in the real world) be delegated to modern autopilot.
I've been working with integrating GPT-5.2 in Unity. It's fantastic at scripting but completely worthless at managing transforms for scene objects. Even with elaborate planning phases it's going to make a complete jackass of itself in world space every time.
LLMs are also wildly unsuitable for real-time control problems. They never will be. A PID controller or dedicated pathfinding tool being driven by the LLM will provide a radically superior result.
Agreed. I’ve found the only reliable architecture for this is treating the LLM purely as a high-level planner rather than a controller.
We use a state machine (LangGraph) to manage the intent and decision tree, but delegate the actual transform math to deterministic code. You really want the model deciding the strategy and a standard solver handling the vectors, otherwise you're just burning tokens to crash into walls.
The right tool would likely be some conventional autopilot software; if you want AI cred you could train a Neural Network which maps some kind of path to the control features of the drone. LLMs are language models -- good for language, but not good for spacial reasoning or navigation or many of the other things you need to pilot a drone.
Why would you want an LLM to identify plants and animals? Well, they're often better than bespoke image classification models at doing just that. Why would you want a language model to help diagnose a medical condition?
It would not surprise me at all if self-driving models are adopting a lot of the model architecture from LLMs/generative AI, and actually invoke actual LLMs in moments where they would've needed human intervention.
Imagine if there's a decision engine at the core of a self driving model, and it gets a classification result of what to do next. Suddenly it gets 3 options back with 33.33% weight attached to each of them and a very low confidence interval of which is the best choice. Maybe that's the kind of scenario that used to trigger self-driving to refuse to choose and defer to human intervention. If that can then first defer judgement to an LLM which could say "that's just a goat crossing the road, INVOKE: HONK_HORN," you could imagine how that might be useful. LLMs are clearly proving to be universal reasoning agents, and it's getting tiring to hear people continuously try to reduce them to "next word predictors."
> Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that".
I think it's fascinating work even if LLMs aren't the ideal tool for this job right now.
There were some experiments with embodied LLMs on the front page recently (e.g. basic robot body + task) and SOTA models struggled with that too. And of course they would - what training data is there for embodying a random device with arbitrary controls and feedback? They have to lean on the "general" aspects of their intelligence which is still improving.
With dedicated embodiment training and an even tighter/faster feedback loop, I don't see why an LLM couldn't successfully pilot a drone. I'm sure some will still fall of the rails, but software guardrails could help by preventing certain maneuvers.
The detection prepass plus text reasoning pipeline is effectively a perception to symbol translation layer, and that is where most of the brittleness will hide. Once you collapse a continuous 3D scene into discrete labels, you lose uncertainty, relative geometry, and temporal consistency unless you explicitly model them. The LLM then reasons over a clean but lossy world model, so action quality is capped by what the detector chose to surface.
The failure mode is not just missed objects, it is state aliasing. Two physically different scenes can map to the same label set, especially with occlusion, depth ambiguity, or near boundary conditions. In control tasks like drone navigation, that can produce confident but wrong actions because the planner has no access to the underlying geometry or sensor noise. Error compounds over time since each step re-anchors on an already simplified state.
Are you carrying forward any notion of uncertainty or temporal tracking from the vision stage, or is each step a stateless label snapshot fed to the reasoning model?
LLM's seem like the wrong platform to operate a drone in my opinion. I would expect that to be something more like a gaming engine. It should be small, simple, low latency and maybe based on a first person shooter running on insane difficulty. Small enough to fit in a tiny firmware space. It should boot so fast the firmware could be upgraded mid-flight without missing a beat. Give it simple friend or foe and obliterate anything not green.
I am curious how these models would perform and how much energy they'd take to semi-realtime detect objects:
SmolVLM2-500M - Moondream 0.5B/2B/2.5B - Qwen3-VL (3B)
https://huggingface.co/collections/Qwen/qwen3-vl
I am sure this is already worked on in Russia, Ukraine and The Netherlands. A lot can go wrong with autonomous flying.
One could load the VLM on a high end android phone on the drone and have dual control.
In a real world test you would have a tool call for the LLM which is a bit high level like GoTo(object) and the tool calls another program which identities the objects in frame and uses standard programs to go to that.
I can’t really take this too seriously. This seems to me to be a case of asking “can an LLM do X?” Instead, the question is like to see is: “I want to do X, is an LLM this right tool?”
But that said, I think the author missed something. LLMs aren’t great at this type of reasoning/state task, but they are good at writing programs. Instead of asking the LLM to search with a drone, it would be very interesting to know how they performed if you asked them to write a program to search with a drone.
This is more aligned with the strengths of LLMs, so I could see this as having more success.
One day they'll fly to a drone factory, eliminate all the personnel, then start gently shooting at the machinery to create more weaponized drones and then it's all over before you know it!
Gemini 3 is the only model I've found that can reason spatially. The results here are accurate to my experiments with putting LLM NPCs in simulated worlds.
I was surprised that most VLLMs cannot reliably tell if a character is facing left or right, they will confidently lie no matter what you do (even gemini 3 cannot do it reliably). I guess it's just not in the training data.
That said Qwen3VL models are smaller/faster and better "spatially grounded" in pixel space, because pixel coordinates are encoded in the tokens. So you can use them for detecting things in the scene, and where they are (which you can project to 3d space if you are running a sim). But they are not good reasoning models so don't ask them to think.
That means the best pipeline I've found at the moment is to tack a dumb detection prepass on before your action reasoning. This basically turns 3d sims into 1d text sims operating on labels -- which is something that LLMs are good at.
We just need to fine tune these models on Ocarina of Time Water Temple - spatial reasoning solved.
I suspect the latency on Gemini 3 makes it non-viable for a real-time control loop though. Even if the reasoning works, the input token costs would destroy the unit economics pretty quickly. I'd be worried about relying on that kind of API overhead for the critical path.
> the input token costs would destroy the unit economics pretty quickly.
They say this is going to happen to every task after the stop subsidizing token costs.
Not for coding though - I'd buy 4 H200's and stick them in my basement if i had to
To do what?
CODING
Neuro-sama, the V-Tuber/AI actually does a decent job of it. Vedal seems to have cooked and figured out how to make an LLM move reasonably well in VRChat.
Not perfectly, there's a lot abuse of gravity or the lack thereof, but yeah. Neuro has also piloted a Robot Dog in the past.
This is what VLA models are for. They would work much better. Would need a bit of fine tuning but probably not much. Lots of literature out there on using VLAs to control drones.
Did some research, found a model that is exactly that. https://cognitivedrone.github.io/
The Black Mirror speedrun continues
Slaughterbots: https://www.youtube.com/watch?v=O-2tpwW0kmU
Thanks will check this out!
I don't understand. Surely training an LSTM with sensor input is more practical and reasonable way than trying to get a text generator to speak commands to a drone.
Very much depends on what you want to do.
The fact that a language model can „reason“ (in the LLM-slang meaning of the term) about 3D space is an interesting property.
If you give a text description of a scene and ask a robot to perform a peg in hole task, modern models are able to solve them fairly easily based on movement primitives. I implemented this on a UR robot arm back in 2023
The next logical step is, instead of having the model output text (code representing movement primitives), outputting tokens in action space. This is what models like pi0 are doing.
I mean semantically language evolved as an interpretation for the material world, so assuming that you can describe a problem in language, and considering that there exists a solution to said problem that is describable in language, then I'm sure a big enough LLM could do it... but you can also calculate highly detailed orbital maps with epicycles if you just keep adding more... you just don't because it's a waste of time and there's a simpler way.
The latter part is interesting. I'm not sure how the performance of one of those would be once they are working well, but my naive gut feeling is that splitting the language part and the driving part into two delegates is cleaner, safer, faster and more predictable.
note that the control systems you were talking about before (i.e. PID) would probably take hold pretty directly in a tiny network, and exactly because of that limitation, be far less likely to contain 'hallucinations'. object avoidance and path planning are likely similar.
since this is a limited and continuous domain, its a far better one for neural training than natural language. I guess this notion that a language model should be used for 3d motion control is a real indicator about the level of thought going into some of these applications.
On the discussion of the right or wrong tool, I find it possible that the ability to reason towards a goal is more valuable in the long run than an intrinsic ability to achieve the same result. Or maybe a mix of both is the ideal.
This is neat! It's a bit amusing in that I worked on a somewhat similar project for my phd thesis almost 10 years ago, although in that case we got it working on a real drone (heavily customized, based on DJI matrice) in the field, with only onboard compute. Back then it was just a fairly lightweight CNN for the perception, not that we could've gotten much more out of the jetson TX2.
Why would you want an LLM to fly a drone? Seems like the wrong tool for the job -- it's like saying "Only one power drill can pound roofing nails". Maybe that's true, but just get a hammer
There are almost endless reasons why. It's like asking why would you want a self-driving car. Having a drone to transport things would be amazing, or to patrol an area. LLMs can be helpful with object identification, reacting to different events, and taking commands from users.
The first thought I had was those security guard robots that are popping up all over the place. if they were drones instead, and LLM talked to people asking them to do/not-do things, that would be an improvement.
Or an waiter drone, that takes your order in a restaurant, flies to the kitchen, picks up a sealed and secured food container, flies it back to the table, opens it, and leaves. It will monitor for gestures and voice commands to respond to diners and get their feedback, abuse, take the food back if it isn't satisfactory,etc...
This is the type of stuff we used to see in futuristic movies. It's almost possible now. glad to see this kind of tinkering.
You could have a program, not LLM-based but could be ANN, for flying and an LLM for overseeing; the LLM could give the program instructions to the pilot program as a (x,y,z) directions. I mean currently autopilots are typically not LLMs, right?
You describe why it would be useful to have an LLM in a drone to interact with it but do not explain why it is the very same LLM that should be doing the flying.
I'm not OP, I don't know what specific roles the LLM should be using, but LLMs are great with object recognition, and using both text (street signs,notices,etc..) and visual cues to predict the correct response. The actual motor control i'm sure needs no LLMs, but the decision making could use any number of solutions, I agree that an LLM-only solution sounds bad, but I didn't do the testing and comparison to be confident in that assessment.
The point is that you don't need an LLM to pilot the thing, even if you want to integrate an LLM interface to take a request in natural language.
An LLM that can't understand the environment properly can't properly reason about which command to give in response to a user's request. Even if the LLM is a very inefficient way to pilot the thing, being able to pilot means the LLM has the reasoning abilities required to also translate a user's request into commands that make sense for the more efficient, lower-level piloting subsystem.
That’s a pretty boring point for what looks like a fun project. Happy to see this project and know I am not the only one thinking about these kinds of applications.
We don't need a lot of things, but new tech should also address what people want, not just needs. I don't know how to pilot drones, nor do I care to learn how to, but I want to do things with drones, does that qualify as a need? Tech is there to do things for us we're too lazy to do.
There are two different things:
1. a drone that you can talk to and fly on its own
2. a drone where the flying is controlled by an LLM
(2) is a specific instance of the larger concept of (1).
You make an argument that 1 should be addressed, which no one is denying in this thread - people are arguing that (2) is a bad way to do (1).
You're considering "talking to" a separate thing, I consider it the same as reading street signs or using object recognition. My voice or text input is just one type of input. Can other ML solutions or algorithms detect a tree (same as me telling it there is a tree,yaw to the right), yes, can LLMs detect a tree and determine what course of action to take? also true. Which is better? I don't know, but I won't be quick to dismiss anyone attempting to use LLMs.
Definitely maybe - but then we are discussing (2), i.e. "what is the right technical solution to solve (1)".
Your previous comment was arguing that (1) is great (which no one denies in this thread, and it is a different discussion about what products are desirable rather than how to build said product) in an answer to someone arguing (2).
I don't think you understand what an "LLM" is. They're text generators. We've had autopilot since the 1930s that relies on measurable things... like PID loops, direct sensor input. You don't need the "language model" part to run an autopilot, that's just silly.
You see to be talking past him and ignoring what they are actually saying.
LLMs are a higher level construct than PID loops. With things like autopilot I can give the controller a command like 'Go from A to B', and chain constructs like this to accomplish a task.
With an LLM I can give the drone/LLM system complex command that I'd never be able to encode to a controller alone. "Fly a grid over my neighborhood, document the location of and take pictures of every flower garden".
And if an LLM is just a 'text generator' then it's a pretty damned spectacular one as it can take free formed input and turn it into a set of useful commands.
They are text generators, and yes they are pretty good, but that really is all they are, they don't actually learn, they don't actually think. Every "intelligence" feature by every major AI company relies on semantic trickery and managing context windows. It even says it right on the tin; Large LANGUAGE Model.
Let me put it this way: What OP built is an airplane in which a pilot doesn't have a control stick, but they have a keyboard, and they type commands into the airplane to run it. It's a silly unnecessary step to involve language.
Now what you're describing is a language problem, which is orchestration, and that is more suited to an LLM.
"they don't actually learn"
Give the LLM agent write acces to a text file to take notes and it can actually learn. Not really realiable, but some seem to get useful results. They ain't just text generators anymore.
(but I agree that it does not seem the smartest way to control a plane with a keyboard)
If thats youre definition of learning, my casio FX has an "ans" feature that "learns" from earlier calculations!!
Can that "ans" variable influence the general way your casio does future calculations?
I don't think so. But with a AI agent it can.
Sure, they still don't have real understanding, but calling this technology mere text generators in 2026 seems a bit out of the loop.
My confusion maybe? Is this simulator just flying point a to b? Seems like it’s handling collisions while trying to locate the targets and identify them. That seems quite a bit more complex than what you are describing has been solved since the 1930s.
LLMs can do chat-completion, they don't do only chat completion. There are LLMs for image generation, voice generation, video generation and possibly more. The camera of a drone inputs images for the LLM, then it determines what action take based on that. Similar to if you asked ChatGPT "there is a tree in this picture, if you were operating a drone, what action would you take to avoid collision", except the "there is a tree" part is done by the LLMs image recognition, and the sys prompt is "recognize objects and avoid collision", of course I'm simplifying it a lot but it is essentially generating navigational directions under a visual context using image recognition.
> There are LLMs for image generation,
That part isn’t handled by an LLM
> voice generation,
That part isn’t handled by an LLM
> video generation
That part isn’t handled by an LLM
Yes it can be, and often is. Advanced voice mode in chatGPT and the voice mode in Gemini are LLMs. So is the image gen in both chatGPT and Gemini (Nano Banana).
What is it handled by? I'm honestly curious, there are models specifically labeled as for those tasks.
"You don't need the "language model" part to run an autopilot, that's just silly."
I think most of us understood that reproducing what existing autopilot can do was not the goal. My inexpensive DJI quadcopter has an impressive abilities in this area as well. But, I cannot give it a mission in natural language and expect it to execute it. Not even close.
You want a self driving car
You don't want an LLM to drive a car
There is more to "AI" than LLMs
Waymo is certainly interested in using LLMs/VLMs for this purpose.
https://waymo.com/research/emma/
https://waymo.com/blog/2024/10/introducing-emma
https://waymo.com/blog/2025/12/demonstrably-safe-ai-for-auto...
I don't mind someone trying LLMs to see if they can do better than existing ML solutions.
Both of those proposed uses are bad things that are worse than what they would replace.
Because we’re interested in AGI (emphasis on general) and LLM’s are the closest thing to AGI that we have right now.
Using an LLM is the SOTA way to turn plain text instructions into embodied world behavior.
Charitably, I guess you can question why you would ever want to use text to command a machine in the world (simulated or not).
But I don't see how it's the wrong tool given the goal.
SOTA typically refers to achieving the best performance, not using the trendiest thing regardless of performance. There is some subtlety here. At some point an LLM might give the best performance in this task, but that day is not today, so an LLM is not SOTA, just trendy. It's kinda like rewriting something in Rust and calling it SOTA because that's the trend right now. Hope that makes sense.
>Using an LLM is the SOTA way to turn plain text instructions into embodied world behavior.
>SOTA typically refers to achieving the best performance
Multimodal Transformers are the best way to turn plain text instructions to embodied world behavior. Nothing to do with being 'trendy'. A Vision Language Action model would probably have done much better but really the only difference between that and the models trialed above is training data. Same technology.
I don’t think trendy is really the right word and maybe it’s not state of the art but a lot of us in the industry are seeing emerging capabilities that might make it SOTA. Hope that makes sense.
LLMs are indeed the definition of trendy (I've found using Google Trends to dive in is a good entry point to get a broad sense of whether something is "trendy")! Basically the right way to think about it is that something can be promising, and demonstrate emerging capabilities, but but those things don't make something SOTA, nor do they make it trendy. They can be related though (I expect everything SOTA was once promising and emerging, but not everything promising or emerging became SOTA). It's a subtlety that isn't super easy to grasp, but (and here is one area I think an LLM can show promise) an LLM like ChatGPT can help unpick the distinctions here. Still, it's slightly nuanced and I understand the confusion.
I think the point may have flown over your head. I am suggesting you are being dismissive with a distinct lack of thought on your reply. Like said I don’t think state of the art is the right way to describe it but I think trendy is equally wrong from the other side of the spectrum. Models that can deal with vision have some really interesting use cases and ones that can be valuable, in a lot of ways I would say state of the art could describe it but I know to folks that are hopelessly negative, it’s a hard reach so I was trying to balance it for you. Hope that makes sense.
Yeah, it feels a bit like asking "which typewriter model is the best for swimming".
> Why would you want an LLM to fly a drone?
We are on HACKER news. Using tools outside the scope is the ethos of a hacker.
When your only tool is a hammer, every problem begins to resemble a nail.
It's a great feature to tell my drone to do a task in English. Like "a child is lost in the woods around here. Fly a search pattern to find her" or "film a cool panorama of this property. Be sure to get shots of the water feature by the pool." While LLMs are bad at flying, better navigation models likely can't be prompted in natural language yet.
What you're describing is still ultimately the "view" layer of a larger autopilot system, that's not what OP is doing. He's getting the text generator to drive the drone. An LLM can handle parsing input, but the wayfinding and driving would (in the real world) be delegated to modern autopilot.
The system prompt for the drone is hilarious to me. These models are horrible at spatial reasoning tasks:
https://github.com/kxzk/snapbench/blob/main/llm_drone/src/ma...
I've been working with integrating GPT-5.2 in Unity. It's fantastic at scripting but completely worthless at managing transforms for scene objects. Even with elaborate planning phases it's going to make a complete jackass of itself in world space every time.
LLMs are also wildly unsuitable for real-time control problems. They never will be. A PID controller or dedicated pathfinding tool being driven by the LLM will provide a radically superior result.
Agreed. I’ve found the only reliable architecture for this is treating the LLM purely as a high-level planner rather than a controller.
We use a state machine (LangGraph) to manage the intent and decision tree, but delegate the actual transform math to deterministic code. You really want the model deciding the strategy and a standard solver handling the vectors, otherwise you're just burning tokens to crash into walls.
What’s the right tool then?
This looks like a pretty fun project and in my rough estimation a fun hacker project.
The right tool would likely be some conventional autopilot software; if you want AI cred you could train a Neural Network which maps some kind of path to the control features of the drone. LLMs are language models -- good for language, but not good for spacial reasoning or navigation or many of the other things you need to pilot a drone.
So you are suggesting building a full featured package that is nontrivial compared to this fun excitement?
Vision models do a pretty decent job with spatial reasoning. It’s not there yet but you’re dismissing some interesting work going on.
Why would you want an LLM to identify plants and animals? Well, they're often better than bespoke image classification models at doing just that. Why would you want a language model to help diagnose a medical condition?
It would not surprise me at all if self-driving models are adopting a lot of the model architecture from LLMs/generative AI, and actually invoke actual LLMs in moments where they would've needed human intervention.
Imagine if there's a decision engine at the core of a self driving model, and it gets a classification result of what to do next. Suddenly it gets 3 options back with 33.33% weight attached to each of them and a very low confidence interval of which is the best choice. Maybe that's the kind of scenario that used to trigger self-driving to refuse to choose and defer to human intervention. If that can then first defer judgement to an LLM which could say "that's just a goat crossing the road, INVOKE: HONK_HORN," you could imagine how that might be useful. LLMs are clearly proving to be universal reasoning agents, and it's getting tiring to hear people continuously try to reduce them to "next word predictors."
Did you read his post?
He answers your question
> Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that".
https://news.ycombinator.com/newsguidelines.html
I disagree. The nearest justification is:
> to see what happens
Isn't that the epitome of the hacker spirit?
"Why?" "Because I can!"
I think it's fascinating work even if LLMs aren't the ideal tool for this job right now.
There were some experiments with embodied LLMs on the front page recently (e.g. basic robot body + task) and SOTA models struggled with that too. And of course they would - what training data is there for embodying a random device with arbitrary controls and feedback? They have to lean on the "general" aspects of their intelligence which is still improving.
With dedicated embodiment training and an even tighter/faster feedback loop, I don't see why an LLM couldn't successfully pilot a drone. I'm sure some will still fall of the rails, but software guardrails could help by preventing certain maneuvers.
The detection prepass plus text reasoning pipeline is effectively a perception to symbol translation layer, and that is where most of the brittleness will hide. Once you collapse a continuous 3D scene into discrete labels, you lose uncertainty, relative geometry, and temporal consistency unless you explicitly model them. The LLM then reasons over a clean but lossy world model, so action quality is capped by what the detector chose to surface.
The failure mode is not just missed objects, it is state aliasing. Two physically different scenes can map to the same label set, especially with occlusion, depth ambiguity, or near boundary conditions. In control tasks like drone navigation, that can produce confident but wrong actions because the planner has no access to the underlying geometry or sensor noise. Error compounds over time since each step re-anchors on an already simplified state.
Are you carrying forward any notion of uncertainty or temporal tracking from the vision stage, or is each step a stateless label snapshot fed to the reasoning model?
LLM's seem like the wrong platform to operate a drone in my opinion. I would expect that to be something more like a gaming engine. It should be small, simple, low latency and maybe based on a first person shooter running on insane difficulty. Small enough to fit in a tiny firmware space. It should boot so fast the firmware could be upgraded mid-flight without missing a beat. Give it simple friend or foe and obliterate anything not green.
I am curious how these models would perform and how much energy they'd take to semi-realtime detect objects: SmolVLM2-500M - Moondream 0.5B/2B/2.5B - Qwen3-VL (3B) https://huggingface.co/collections/Qwen/qwen3-vl
I am sure this is already worked on in Russia, Ukraine and The Netherlands. A lot can go wrong with autonomous flying. One could load the VLM on a high end android phone on the drone and have dual control.
A better way would be a VLA as opposed to a VLM. VLAs are meant to take action, where as vlms are for geneeral use. https://cognitivedrone.github.io/
At least he's not feeding real drones to the coyotes... oh, there's a link in the readme https://github.com/kxzk/tello-bench
I’m guessing googles model has extensive Minecraft sandbox mode YouTube vids in its training which would exactly this perspective
> I gave 7 frontier LLMs a simple task: pilot a drone through a 3D voxel world and find 3 creatures.
> Only one could do it.
If I understood the chart correctly, even the successful one only found 1/6 of the creatures across multiple runs.
No science detected.
Without comparison to some null hypothesis (a random policy), this article is hogwash.
Given that all the other agents failed to find any creatures, it's hard to imagine that a random policy would except by extreme coincidence.
It is possible to be consistently wrong in a way that randomness is not.
For some problems, randomness outperforms incompetent reasoning
In a real world test you would have a tool call for the LLM which is a bit high level like GoTo(object) and the tool calls another program which identities the objects in frame and uses standard programs to go to that.
https://cognitivedrone.github.io/
Interesting. In some benchmarks I even see flash outperforming thinking in general reasoning.
Gemini Flash beats Gemini Pro? How does that work?
Gemini Pro, like the other models, didn't even find a single creature.
This sounds like a good way to get your drone shot down by a Concerned Citizen or the military.
LLMs are trained on text. Why would we expect them to understand a visual and tactile 3D world?
Because they’re also multimodal vLLMs.
I can’t really take this too seriously. This seems to me to be a case of asking “can an LLM do X?” Instead, the question is like to see is: “I want to do X, is an LLM this right tool?”
But that said, I think the author missed something. LLMs aren’t great at this type of reasoning/state task, but they are good at writing programs. Instead of asking the LLM to search with a drone, it would be very interesting to know how they performed if you asked them to write a program to search with a drone.
This is more aligned with the strengths of LLMs, so I could see this as having more success.
LLMs flying weaponized drones is exactly how it starts.
https://www.youtube.com/watch?v=O-2tpwW0kmU
One day they'll fly to a drone factory, eliminate all the personnel, then start gently shooting at the machinery to create more weaponized drones and then it's all over before you know it!
It's pretty entertaining seeing the plot lines and ficticious history in The Terminator movies actually happening in real time.
"drone"