The labs are spending huge money collecting "multisensory, perception-action coupled training data" (eg, there is the one in NY that gives you free cleaning in return for video data from the cleaner).
Edit: The Gemini Robotics blog post has a video of it tying knots too. That's pretty good.
You can point to the bitter lesson to support your claim, but on the other hand I can point to the massive investment in capital and time to get self-driving cars viable to support my more bearish view.
Yesterday evening I rode 12 miles in a Waymo actively dodging pedestrians and obstacles dynamically in an open ended environment. Multiples better experience than the two Ubers I had later that evening.
I'm not talking about the state of today's self-driving cars, I'm talking about how we got to here. Also don't forget the overarching claim regarding lack of sensor and actuator fidelity in the parent comment; self-driving cars in contrast have expensive LIDAR in them and we still don't think camera-based self-driving cars are safe enough.
I don't know how you can read my comment, not respond to my comment saying that today's self-driving cars use LIDAR, and continue to reiterate your point.
I don't think I was clear and explicit, it being tedious to write, and I apologize for that. I also apologize for shifting the goalposts as I had not written out my own position, which is not exactly in the "grandparent commenter"'s position (that I had not previously given enough attention understanding), but it is also not in agreement with yours. I don't mean to say that we are not presently in the "bitter lesson" (your idea of what the bitter lesson says) regime. I definitely think that a lot of progress can be done right now by emphasizing the humanoid robotics platform as a foundation. What I mean to say is that I don't know if that platform with the hardware we have today is sufficient for parity with human housekeeping tasks in the domains that we wish it to have parity. The bitter lesson itself (not your understanding of it) is in fact silent on this as it is in relation to feature engineering, where it is a clear point, but you seem to be adapting it uncritically wholesale to mean something more than what it is written about. My position is that, it is unclear whether today's sensor platform is sufficient for parity. It is less strong than the blog post author's "Why Today’s Humanoids Won’t Learn", it is a "We can't say whether or not today's humanoids will learn", but it is something that also contradicts a "the bitter lesson means today's humanoids will learn" thesis.
The self-driving car supports my claim, because after so much investment in capital and time, we ended up with a car with comparatively expensive LIDAR sensors as our preferred platform.
Tesla's entire fleet runs on raw cameras. Including the driverless Robotaxi vehicles - which are basically a 1:1 match to how Waymo operates.
Plenty of hecklers were saying "you can't self-drive on cameras", and some still try. But Tesla's self-driving on cameras, and it seems to work fine. While Waymo's self-driving on fat sensor stacks, and it also seems to work fine. Sensors don't seem to be a differentiator of self-driving performance.
I don't think anything about self-driving tech supports your claim. Tesla was bullish on AI all the way, and Waymo has also shifted towards highly integrated end to end AI. It's the AI advances that make self-driving tractable - not anything else.
It is amusing to observe that the tech marketing of today are milking the shit out of this trick.
The trick being to tread continuously through some non-obvious happy path. And average people will be convinced that you really have some breakthrough tech.
But hey, this is not something new. Magicians were taking advantage of such things for centuries ..
1. Make some thing that work in very limited of amount of real world cases
2. Deploy it somewhere where it won't encounter things it won't handle.
3. Market the shit out of the above fact and how well it work there.
4. Let the naive population who have a tendency to take one look, and imagine how it will automatically progress to some arbitrary influx point.
5. Get a lot of funding from people in point 4 and feed it to point 3, and keep going.
Why couldn’t this trick be pulled off two decades ago?
After all autonomous vehicles has been well funded research since the 1980s the DARPA grand challenge being one of the previously most important benchmarks.
I think you might just need a history lesson friend
Action LLMs work by generating text underneath. Just some higher level software interprets the text generated and do some action. So the immediate inference result is still text.
Obviously there is a lot of variety in architecture, but in the prototypical example there are vision and languages encoders and an action decoder which decodes direction into action steps. Eg, Hugging Face SmolVLA:
> Specifically, the VLM processes sensorimotor states, including images from multiple RGB cameras, and a language instruction describing the task. In turn, the VLM outputs features directly fed to the action expert, which outputs the final 3 continuous actions.[1]
Or NVidia's GR00T N1:
> A diffusion transformer (DiT) processes the robot’s proprioceptive state and action, which are then cross-attended with image and text tokens from the Eagle-2 VLM backbone to output the denoised motor actions.[2]
Depends entirely on VLA arch. Some have dedicated action diffusion heads that work in a standalone non-text action output space. Much like an LLM can either use an external TTS or have audio output heads attached to it directly for native S2S.
But your entire premise is wrong regardless of that.
Even if VLAs were forever bound to outputting text, you'd have to prove that they're fundamentally incapable of emitting text that maps to useful action sequences. No proof of that whatsoever - and plenty of empirical evidence suggests otherwise. Even non-specialist LLMs like ChatGPT are getting better at controlling robots and navigating 3D environments, if slowly.
>But your entire premise is wrong regardless of that.
You don't understand what I am saying. The crux of your misunderstanding is here
>emitting text that maps to useful action sequences
If you have a static mapping from text to action, then you are throwing away all the advantage of using an AI. The whole point of AI is that you can get an output from an input without explicit mapping. So If you use explicit mapping anywhere in the chain, then you lose most of the advantage of using the AI.
So if your hardware, physical vocabulary is limited, like move left/right/up/down then what you say could work. But something that have the dexterity of a human form, this vocabulary is nearly infinite. You won't be able to use explicit mapping there.
You can literally have an LLM output target joint angles. As text. To be decoded by an explicit decoder, and executed by the robot. Some early VLAs did exactly that.
Your entire premise is wrong.
Modern action decoders are different, and usually take the form of neural networks trained end to end jointly with the rest of the model. Not fundamentally more expressive, just more in line with what we want.
I'm repeating "what you claim to be impossible was done 3 years ago and was already replaced with better versions of the same idea and you are hilariously out of touch".
As I suspected you are fooled by this video and imagine it to be capable of much more than what is shown. This video is pretty non-marketing and is quite straight to the point. But that does not prevent you from being awed!
So What is LLM is used here for? It is used for mere translation between different robots. So it is mostly symbolic translation.
What I am talking about is to translation LLM inference directly to movements. For example, if you ask an LLM, how do I open the microwave door? It will list the steps. I am talking about a system that can go from "put the thing in the microwave", to action steps, without having to never once demonstrate it physically, and do it just from LLM inference.
In short, the way LLMs used here is not (categorically) the way I was asking about.
The thing literally has a diffusion "action expert" sit in the same attention system as a pre-trained VLM. And the VLM itself is ALSO trained to generate raw actions as a part of the training recipe (the first paper) - it just doesn't do it at inference time. What the "action expert" does is parallelize the action generation process - based on VLM's internal states.
It's exactly the thing you claimed to be impossible. Described in detail in a paper from 2025. What's your excuse?
I have not overlooked anything. I had imagined that this "mapping", to have any power, would also need to be handled by an LLM (action expert). But here is the problem with that. That would not be as "intelligent" as an LLM....And you can't make it as smart as the LLM because there is not a similarly huge training data on which LLMs are trained on..
The backbone of the VLA there is literally a pre-trained Gemma model. And a small one at that.
You already downgraded your claims from "LLMs are irrelevant to robotics" to a measly "you can't train a useful robotics LLM because there's not enough data". And you say that while looking at an LLM that was pre-trained on all of internet scraped and only then reused for robotics.
Both the pool of robotics-relevant data and the performance of foundation model LLMs grow over time. All the companies that are serious about robotics are serious about scaling up data collection.
I'm not going to claim that this "LLM core" approach is the best approach to AI robotics possible - but if you're betting on it failing outright, you're going to be fighting uphill.
Eg, even LeRobot (without proper fingers) can fold clothes now: https://www.youtube.com/watch?v=dPe9v4gqbdg
The labs are spending huge money collecting "multisensory, perception-action coupled training data" (eg, there is the one in NY that gives you free cleaning in return for video data from the cleaner).
Edit: The Gemini Robotics blog post has a video of it tying knots too. That's pretty good.