lyapunova

Thank you for your rebuttal. It is good to think about the "just a stochastic parrot" thing. In many ways this is true, but it might not be bad. I'm not against replay. I'm just pointing out that I would not start with an _affordable_ 20k robot with fairly undeveloped engineering fundamentals. It's kind of like trying to dig a foundation to your house with a plastic beach shovel. Could you do it? Maybe, if you tried hard enough. Is it the best bet for success? doubtful.

Thanks for maintaining benchmarks here. Is there a github repo that might accompany the benchmarks that I could take a look at / reproduce?

So, a couple things here.

It is true that replay in the world frame will not handle initial position changes for the shirt. But if the commands are in the frame of the end-effector and the data is object-centric, replay will somewhat generalize.(Please also consider the fact that you are watching the videos that have survived the "should I upload this?" filter.)

The second thing is that large-scale behavior cloning (which is the technique used here), is essentially replay with a little smoothing. Not bad inherently, but just a fact.

My point is that there was an academic contribution made back when the first aloha paper came out and they showed doing BC on low-quality hardware could work, but this is like the 4th paper in a row of sort of the same stuff.

Since this is YC, I'll add - As an academic (physics) turned investor, I would like to see more focus on systems engineering and first-principles thinking. Less PR for the sake of PR. I love robotics and really want to see this stuff take off, but for the right reasons.

To be honest, I actually really like the visual delivery here. It's especially helpful for understanding what's going on with computer vision problems. Please make more!

I can appreciate that, but also they are recording and replaying motor signals from specific teleoperation demonstrations. Something that _was_ possible in the 1950s. You might say that it is challenging to replay demonstrations well on lower-quality hardware. And so there is academic value in trying to make it work on worse hardware, but it would not be my goto solution for real industry problems. E.g. this is not a route I would fund for a startup, for example.

To be honest, most researchers in applied ML in the bay say the opposite. If you are trying to be nimble and prototype, use pytorch. If you're trying to gain some optimizations as you near deployment, rewrite in Jax.

Let me say, he's a great teacher! I took a CV class with him. He should teach more, and take it seriously.

Being a popular AI influencer is not necessarily correlated with being a good researcher though. And I would argue there is a strong indication that it is negatively correlated with being a good business leader / founder.

Here's to hoping he chills out and goes back to the sorely needed lost art of explaining complicated things in elegant ways, and doesn't stray too far back into wasting time with all the top sheisters of the valley.

Edit: the more I think about it, the more I realize that it probably screws with a person to have their tweets get b-lined to the front page of hackernews. It makes you a target for offers and opportunities because of your name/influence, but not necessarily because of your underlying "best fit"

The headline, the picture, the article --- it would be easier to take them seriously if they just made the tools work and stopped posing for band pictures.

I am happy to have the tools, but the hype, the valuation, the "we have solved everything" mentality. It's just so offputting.

I think the problem with stuff like this is that in many cases the authors have an idea like "LLMS + ???? = Operating System !"

which is not that hard of an idea to come up with. Generally in each instance of LLM + ???? = "thing", the first papers that come out to fill the ???? with an answer do that in a rush to get on arxiv and so naturally the work is lackluster (since they have barely had time to think about an actual good solution for the ????).

I am really impressed by this work. If only Disney needed an empirical physicist...

I appreciate the conciseness of this article. Thank you for sharing. I agree. The kitchen sink data strategy appears to be fairly inefficient with current model architectures.

Twitter is just embarrassing now. I haven't used it in over a year now. Turns out it's absolutely unnecessary for a normal, well-informed lifestyle.

The word on the street among the researchers I know from my grad school days is that this is basically a bunch of datasets that were created earlier on for specific papers, and the subset of researchers are repackaging them as a kind of weird academia twitter winnertakeall thing.

The author list also reminds me of the embarrassing practices of the physics community…

Nothing wrong with releasing a webpage with a list of datasets, but the press and need for a whole big announcement that claims it will change robotics makes them seem desperate.