Comment by ACCount37 | Hacker News Reader

Comment by ACCount37 | Hacker News Reader

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Every single task that was easy and economical to offload to a single purpose robot arm bolted down to the floor was already offloaded to a single purpose robot arm bolted down to the floor.

What remains is: all those quirky little one-off processes that aren't very amenable to "robot arm" automation, aren't worth the process design effort to make them amenable to it, and are currently solved by human labor.

Thus, you design new solutions to target that open niche.

Humans aren't perfect at anything, but they are passable at everything. Universal worker robots attempt to replicate that.

"A drop-in replacement for simple human labor" is a very lucrative thing, assuming one could pull it off. And that favors humanoid hulls.

Not that it's the form that's the bottleneck for that, not really. The problem of universal robots is fundamentally an AI problem. Today, we could build a humanoid body that could mechanically perform over 90% of all industrial tasks performed by humans, but not the AI that would actually make it do it.

My impression is that a big part of the reason for the sudden boom in humanoid robots is that they lend themselves particularly well to RL based training using human-made training footage using VR. It’s much easier to have a robot broadly copy human actions if the robot looks like a human, instead of having to first translate the human action to your robot arm equivalent.

That is certainly a factor, but you also have to take into account that all these tasks in the factories are now centered around the human form because humans are doing them.

The big part is the rise of modern AI in general.

The success of large multipurpose AI models trained on web-scale data pushed a lot of people towards "cracking general purpose robot AI might be possible within a decade".

Whether transfer learning from human VR/teleop data is the best way to do it remains uncertain - there are many approaches towards training and data collection. Although transfer learning from web-scale data, teleoperation and "RL IRL" are common - usually on different ends of the training pipeline.

Tesla got the memo earlier than most, because Musk is a mad bleeding edge technology demon, but many others followed shortly before or during the public 2022 AI boom.

This framing clarifies something people get wrong about humanoid robots. The competition isn't "humanoid vs. better robot" — it's "humanoid vs. hiring another person."

And that reframes the economics entirely. You don't need the robot to be better than a human at any given task. You need the total cost of ownership to be lower than a salary, benefits, turnover, and training. That's a much easier bar to clear once the AI catches up to the body.

The interesting question is whether the AI problem gets solved generally (one model that can do everything) or whether we end up with task-specific AI in a general-purpose body — basically the robot arm paradigm wearing a humanoid suit.

Em-dashes aside, I favor "one model that can do everything" in principle because scaling laws and distillation exist, and in practice because "one model that you can point at any problem" is a massive operational advantage.

If you can get 5 specialist models that can use the same robot body, you can also get 1 generalist model with more capacity and fold the specialists into it. If you have the in-house training that made those specialists, apply them to the generalist instead, the way we give general purpose AIs coding-specific training. If you don't, take the specialists as is and distill from them.

If you do it right, transfer learning might even give you a model that generalizes better and beats the specialists at their own game. Because your "special" tasks have partial subtask overlap that you got stronger training for, and contributed to diversity of environments. Robotics AI is training data starved as a rule.

Same kind of lesson we learned with LLM specialists - invest into a specialist model and watch the next gen generalists with better data and training crush it.

> Every single task that was easy and economical to offload to a single purpose robot arm bolted down to the floor was already offloaded to a single purpose robot arm bolted down to the floor.

What about doing dishes? That could be done with one arm. Maybe not easy and economical yet, but could be.

There is plenty that has not been seen through.

Laundry folding machines are not in wide distribution.

Robots to put away laundry?

Etc. lots of mundane tasks.

Yeah I think there’s plenty of room for more bolted arm robots, it’s just similar to the humanoids they need better AI. There’s also room for more optimisation on the entire system design around more specialized robots. I think some industries work really well for that kind of revamp, and have already begun doing so. Others are waiting for the cost curve to fall for it to be worth the investment

Yes that's pretty much it. Some people from boston dynamics were talking on a podcast. And they were saying that they sat down with toyota and figured out they could automate all the tasks in a factory, but it would take 10000 man years or something and toyota makes new trims every six months so you need about 10000 man years every six months or so.

It's the flexibility and adaptability with minimum training that's required.

I think this is the podcast you mentioned:

https://youtu.be/SRZ9E48B6aM?si=K_wwvu97agBZpFTa

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