>> Quantitatively, the QT-Opt approach succeeded in 96% of the grasp attempts across 700 trial grasps on previously unseen objects. Compared to our previous supervised-learning based grasping approach, which had a 78% success rate, our method reduced the error rate by more than a factor of five.
https://research.google/blog/scalable-deep-reinforcement-lea...
That was in 2018.
So what do you think, is vision-based robotic manipulation and grasping a solved problem, seven years later? Is QT-Opt now an established industry standard in training robots with RL?
Or was that just another project that was announced with great fanfare and hailed as a breakthrough that would surely lead to great increase of capabilities... only to pop, fizzle and disappear in obscurity without any real-world result, a few years later? Like most of DeepMind's RL projects do?
https://www.youtube.com/watch?v=x-exzZ-CIUw
It looks pretty awesome. Let's see what happens.
https://youtu.be/03p2CADwGF8?si=BXeWXqu1_3WMS4yy
A robot assembling a puzzle with machine vision!
And it's only from the 1970's.
But, like you say- let's wait and see. I always do the former but I'm still waiting for the latter.
I encourage you to read deepmind's work with robots.