[dkislyuk]

Moravec's paradox is the usual counterargument given to this line of reasoning. We've had far less progress in embodied robotics, where a robot has to interact with the real world in any kind of generalized, tactile way, compared to visual, audio, and language processing tasks. The history of AI is littered with predictions that <a reasoning or computation AI breakthrough> will lead to a humanoid robot, and the predictions always end up in the regime of ~real world data collection and integration is harder than we thought.

Maybe this time it's different, and maybe it's not, but that's why most recent robotics predictions fail to convince the ML industry broadly.

[lukeschlather]

I don't think that's true. I recall when Amazon acquired Kiva robotics for their warehouse operations one of the people involved said something like "If you want something that can pick assorted objects out of a box and put them in another box, I'll need a NASA research team and 5 years, but moving the boxes around, we can do that with our Kiva robots." Here we are 10 years later and Amazon does in fact have the picker arms, though I'm not sure how production-ready they are, Amazon has demoed them.

Honestly I think there's been very dramatic improvements in robotics alongside ChatGPT but ChatGPT is easy to demo with nothing but an internet connection so it's just a lot less visible.

[smoldesu]

That first quote is right on-the-money, in my experience with competitive FRC and automation. It's extremely easy to work with a limited set of parameters like a cardboard box; you can easily estimate object volume and bounding-box collision in software. Making a robot that manipulates millions of Amazon products is a suicide mission by comparison; especially if you expect it to behave consistently.

Computer vision, AI and inverse kinematics have all come a long ways in the past few years. That being said, it's still easier by an order of magnitude to design the box-pushing robot.