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"This is a perfect example of how AI is taking over the world by storm."

Because they taught a robot hand to rotate a cube well... ?

Take it from an AI researcher, it's one thing to make a demo of a technique solving a narrow very simple problem and quite another to use that technique to solve a real world need.

"Any new job will require some sort of skills out the set of skills that can be trained" - even if we do reach the level of AI there this would actually hold, we are NOWHERE near that today, and this result certainly does not demonstrate we are.



Dextrous manipulation is a big deal though. Typical stuff we've seen like generating synthetic faces using GANs is not commercially useful, but eye-hand coordination is 90% of commercial activity worldwide.


That is true (and I am well aware of this, as I do research on robotic grasping https://sites.google.com/view/task-oriented-grasp ). But, this would very hard to generalize to more complex manipulation where the object is not already in-hand etc. ; it's yet another piece of the puzzle, but it by itself does not get us that much closer to general purpose training of robust complex manipulation.


Well, i can see that you are a expert in this field, but i still have to say the method they demonstrate, domain randomization is a big deal because that can then be applied to anything else like picking up stuff. Domain randomization is a year old thing now, but it's a big deal - if you can build a "crappy" simulator for it, you will be able to do anything. I feel like domain randomization isnt being promoted enough in the media


sure, but they demonstrate it for a very simple use case of a single object with no background etc. ; domain randomization has yet to be demonstrated to work on more complex environments/tasks


One of the last things not automated at Amazon fulfillment centers is identifying and picking up the item from the shelf brought by the robots which contain it.


sure, and there has already been a ton of work on grasping (see eg DexNet 2.0, Closing the loop for robotic grasping). This stuff is already in the process of being commercialized, and this research does not advance it much.




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