This is interesting for generalized problems ("make me a sandwich") but not useful for most real world functions ("perform x within y space at z cost/speed"). I think the number of people on the humanoid bandwagon trying to implement generalized applications is staggering right now. The physics tells you they will never be as fast as purpose-built devices, nor as small, nor as cheap. That's not to say there's zero value there, but really we're - uh - grasping at straws...
> the core insight: predict in representation space, not pixels
We've been doing this since 2014? Not only that, others have been doing it at a similar scale. e.g. Nvidia's world foundation models (although those are generative).
> zero-shot generalization (aka the money shot)
This is easily beaten by flow-matching imitation learning models like what Pi has.
> accidentally solved robotics
They're doing 65% success on very simple tasks.
The research is good. This article however misses a lot of other work in the literature. I would recommend you don't read it as an authoritative source.
I just wrote a reply to a comment talking about the AI tells this writing has, but it got flagged so my comment disappeared when I hit post. I'll rephrase out of spite:
My first thought upon reading this was that an LLM had been instructed to add a pithy meme joke to each paragraph. They don't make sense in context, and while some terminally online people do speak in memes, those people aren't quoting doge in 2025.
There's also a sense of incoherence in the whole piece. For instance, this section:
"- after: 22 million videos + 1 million images (now we're talking)
they basically hoovered up everything: something-something v2, kinetics, howto100m, and a billion youtube videos"
Was it a billion vids or 22m? It turns out the latter sentence is just rephrasing the list of sources in a cool casual way, and the last one is called YT-Temporal-1B. That's a billion frames of video, not a billion videos.
I don’t know, 400k people are listening to the White House streaming lo-fi hip hop on X right now with cutesy videos of Trump on one side and his executive orders streaming on the other at 4am. I think there’s plenty of people quoting doge in 2025.
If you’re in the US, you likely work with them and they have learned to studiously avoid talking about politics except in vagaries to avoid conflict.
I'm using eigenrobot's (X user) prompt for ChatGPT and the style is very recognizable. Everything lowercase, tone, zoomer abbreviations, esotheric style of jokes.
>> They don't make sense in context, and while some terminally online people do speak in memes, those people aren't quoting doge in 2025.
Cringely, they are. Nobody who isn't desperate to appear cool would write in that terminally grating register, including when using an LLM to do the writing.
My mom said I was throwing away my life watching YouTube all day and clearly I just haven’t been watching YouTube enough. 1 million YouTube videos here I come!
"why didn't we think of this sooner?", asks the article. Not sure who the "we" is supposed to be, but the robotics community has definitely thought of this before. https://robo-affordances.github.io/ from 2023 is one pretty relevant example that comes to mind, but I have recollections of similar ideas going back to at least 2016 or so (many of which are cited in the V-JEPA2 paper). If you think data-driven approaches are a good idea for manipulation, then the idea of trying to use Youtube as a source of data (an extremely popular data source in computer vision for the past decade) isn't exactly a huge leap. Of course, the "how" is the hard part, for all sorts of reasons. And the "how" is what makes this paper (and prior research in the area) interesting.
I don't know. I'm not the expert, but if you've ever tried to a backflip or anything where your toes are above your head, then you'll know that spatial awareness goes well beyond vision. Or if you throw a frisbee for the dog to catch, they don't actually look at it while running; they look, predict position, then move in. Veni, vidi, vici. So any model that "learns physics" just through vision seems flawed from the start. What's your thought there?
Pure vision will never be enough because it does not contain information about the physical feedback like pressure and touch, or the strength required to perform a task.
For example, so that you don't crush a human when doing massage (but still need to press hard), or apply the right amount of force (and finesse?) to skin a fish fillet without cutting the skin itself.
Practically in the near term, it's hard to sample from failure examples with videos on Youtube, such as when food spills out of the pot accidentally. Studying simple tasks through the happy path makes it hard to get the robot to figure out how to do something until it succeeds, which can appear even in relatively simple jobs like shuffling garbage.
With that said, I suppose a robot can be made to practice in real life after learning something from vision.
> Pure vision will never be enough because it does not contain information
Say it louder for those in the back!
But actually there's more to this that makes the problem even harder! Lack of sensors is just the beginning. There's well known results in physics that:
You cannot create causal models through observation alone.
This is a real pain point for these vision world models and most people I talk to (including a lot at the recent CVPR) just brush this off as "we're just care if it works." Guess what?! Everyone that is pointing this out also cares that it works! We need to stop these thought terminating cliches. We're fucking scientists.
Okay, so why isn't observation enough? It's because you can't differentiate alternative but valid hypotheses. You often have to intervene! We're all familiar with this part. You control variables and modify one or a limited set at a time. Experimental physics is no easy task, even for things that sound rather mundane. This is in fact why children and animals play (okay, I'm conjecturing here).
We need to mention chaos here, because it's the easiest way to understand this. There's many famous problems that fall into this category like the double pendulum, 3 Body Problem, or just fucking gas molecules moving around. Let's take the last one. Suppose you are observing some gas molecules moving inside a box. You measure their positions at t0 and at T. Can you predict their trajectories between those time points? Surprisingly, the answer is no. You can only do this statistically. There's probably paths but not deterministic (this same logic is what leads to multiverse theory btw). But now suppose I was watching the molecules too, but I was continuously recording between t0 and T. Can I predict the trajectories? Well, I don't need to, I just write it down.
Now I hear you, you're saying "Godelski, you observed!" But the problem with these set of problems is that if you don't observe the initial state you can't predict moving forwards and if you don't have very precise observation intervals you are hit with the same problem. I you turn around while I start a double pendulum you can have as much time as you want when you turn back around, you won't be able to model its trajectories.
But it gets worse still. There are confounding variables. There is coupling. Difficult to differentiate hypotheses via causal ordering. And so so much more. If you ever wonder why physicists do so much math it's because doing that is a fuck ton easier than doing the whole set of testing and then reverse engineering the equations from those observations. But in physics we care about counterfactual statements. In F=ma we can propose new masses and new accelerations and rederive the results. That's the what it is all about. Your brain does an amazing job at this too! You need counterfactual modeling to operate in real world environments. You have to be able to ask and answer "what happens if that kid runs into the street?"
I highly suggest people read The Relativity of Wrong [0]. Its a short essay by Isaac Asimov that can serve as a decent intro, though far from complete. I'm suggesting it because I don't want people to confuse "need counterfactual model" with "need the right answer." If you don't get into metaphysics, these results will be baffling.[1] It is also needed to answer any confusion you might have around the aforementioned distinction.
Tldr:
if you could do it from observation alone, physics would have been solved a thousand years ago
There's a lot of complexity and depth that is easy to miss with the excitement, but it still matters.
I'm just touching the surface here too, and we're just talking about mechanics. No quantum needed, just information loss
39 comments
[ 3.5 ms ] story [ 67.6 ms ] threadhttps://www.videomimic.net/
https://www.videomimic.net/page1.html
if you havent - highly recommended.
> the core insight: predict in representation space, not pixels
We've been doing this since 2014? Not only that, others have been doing it at a similar scale. e.g. Nvidia's world foundation models (although those are generative).
> zero-shot generalization (aka the money shot)
This is easily beaten by flow-matching imitation learning models like what Pi has.
> accidentally solved robotics
They're doing 65% success on very simple tasks.
The research is good. This article however misses a lot of other work in the literature. I would recommend you don't read it as an authoritative source.
My first thought upon reading this was that an LLM had been instructed to add a pithy meme joke to each paragraph. They don't make sense in context, and while some terminally online people do speak in memes, those people aren't quoting doge in 2025.
There's also a sense of incoherence in the whole piece. For instance, this section:
"- after: 22 million videos + 1 million images (now we're talking)
they basically hoovered up everything: something-something v2, kinetics, howto100m, and a billion youtube videos"
Was it a billion vids or 22m? It turns out the latter sentence is just rephrasing the list of sources in a cool casual way, and the last one is called YT-Temporal-1B. That's a billion frames of video, not a billion videos.
If you’re in the US, you likely work with them and they have learned to studiously avoid talking about politics except in vagaries to avoid conflict.
Cringely, they are. Nobody who isn't desperate to appear cool would write in that terminally grating register, including when using an LLM to do the writing.
For example, so that you don't crush a human when doing massage (but still need to press hard), or apply the right amount of force (and finesse?) to skin a fish fillet without cutting the skin itself.
Practically in the near term, it's hard to sample from failure examples with videos on Youtube, such as when food spills out of the pot accidentally. Studying simple tasks through the happy path makes it hard to get the robot to figure out how to do something until it succeeds, which can appear even in relatively simple jobs like shuffling garbage.
With that said, I suppose a robot can be made to practice in real life after learning something from vision.
But actually there's more to this that makes the problem even harder! Lack of sensors is just the beginning. There's well known results in physics that:
This is a real pain point for these vision world models and most people I talk to (including a lot at the recent CVPR) just brush this off as "we're just care if it works." Guess what?! Everyone that is pointing this out also cares that it works! We need to stop these thought terminating cliches. We're fucking scientists.Okay, so why isn't observation enough? It's because you can't differentiate alternative but valid hypotheses. You often have to intervene! We're all familiar with this part. You control variables and modify one or a limited set at a time. Experimental physics is no easy task, even for things that sound rather mundane. This is in fact why children and animals play (okay, I'm conjecturing here).
We need to mention chaos here, because it's the easiest way to understand this. There's many famous problems that fall into this category like the double pendulum, 3 Body Problem, or just fucking gas molecules moving around. Let's take the last one. Suppose you are observing some gas molecules moving inside a box. You measure their positions at t0 and at T. Can you predict their trajectories between those time points? Surprisingly, the answer is no. You can only do this statistically. There's probably paths but not deterministic (this same logic is what leads to multiverse theory btw). But now suppose I was watching the molecules too, but I was continuously recording between t0 and T. Can I predict the trajectories? Well, I don't need to, I just write it down.
Now I hear you, you're saying "Godelski, you observed!" But the problem with these set of problems is that if you don't observe the initial state you can't predict moving forwards and if you don't have very precise observation intervals you are hit with the same problem. I you turn around while I start a double pendulum you can have as much time as you want when you turn back around, you won't be able to model its trajectories.
But it gets worse still. There are confounding variables. There is coupling. Difficult to differentiate hypotheses via causal ordering. And so so much more. If you ever wonder why physicists do so much math it's because doing that is a fuck ton easier than doing the whole set of testing and then reverse engineering the equations from those observations. But in physics we care about counterfactual statements. In F=ma we can propose new masses and new accelerations and rederive the results. That's the what it is all about. Your brain does an amazing job at this too! You need counterfactual modeling to operate in real world environments. You have to be able to ask and answer "what happens if that kid runs into the street?"
I highly suggest people read The Relativity of Wrong [0]. Its a short essay by Isaac Asimov that can serve as a decent intro, though far from complete. I'm suggesting it because I don't want people to confuse "need counterfactual model" with "need the right answer." If you don't get into metaphysics, these results will be baffling.[1] It is also needed to answer any confusion you might have around the aforementioned distinction.
Tldr:
There's a lot of complexity and depth that is easy to miss with the excitement, but it still matters.I'm just touching the surface here too, and we're just talking about mechanics. No quantum needed, just information loss
[0] m3kw9 ↗ Solving robotics is some claim. moneywaters ↗ So video gen models basically can be extrapolated to control robotics ? How long until Veo3 robots take over?