>> 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?