He answers your question

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.

This looks like a pretty fun project and in my rough estimation a fun hacker project.

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.

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."

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.

We are on HACKER news. Using tools outside the scope is the ethos of a hacker.

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.

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.