Neat paper though one major limitation is they trained from real data from 2 micro kitchens.
To get to very high robotic reliability they needed a simulation of many variations of the robot’s operating environment. And a robot with a second arm and more dexterity on it’s grippers.
Basically the paper was not showing a serious attempt to reach production level reliability, just to tinker with a better technique.
I think you can find it interesting: https://ai.googleblog.com/2022/12/rt-1-robotics-transformer-for-real.html?m=1
Neat paper though one major limitation is they trained from real data from 2 micro kitchens.
To get to very high robotic reliability they needed a simulation of many variations of the robot’s operating environment. And a robot with a second arm and more dexterity on it’s grippers.
Basically the paper was not showing a serious attempt to reach production level reliability, just to tinker with a better technique.