Like said by others, the sensor problem is a real one, and personally I don't see a robotic hand matching the dexterity of a human one, within a viable economic cost and sufficient robustness, before a long time (but I could be wrong ).
But for the part that concern physical understanding of the world, adaptability, and basic manipulation, it feels like the field is going through a break through.
The implication might be the apparition of generalist robots, that could perform (in the limit of their dexterity) on a wide range of tasks, commanded by natural language.
Sure, not on pair with human dexterity yet, but as a thought exercise, think of all the things you could do with simple claws, or heavy glove. What if a robot was able to do the same ?
"Matching the dexterity" is a false goal. It's focusing on the wrong things.
As teleoperation readily shows, even a very dumb primitive hand can be salvaged if you get a very advanced control policy to drive it.
Sure, it would still take a lot longer for a well tuned robot with just a pair of UMI-style grippers to tie shoe laces than it would a practiced human. But a robot doesn't have to work faster than a human. "It can do the same task for cheaper" is often good enough.
The right thing to focus on is: AI. We already have robot bodies that are good enough to do many economically valuable tasks. We don't have the AI that would make those bodies perform to their potential. Modern robotics is an AI problem above all.
There are such things as established robotics giants, supplying robotic arms to factories like car plants that put together giant metal panels together and junk food industries assembling muffins into plastic trays and boxing up.
Those companies seem to be rushing to get NVIDIA Isaac systems and some even buying DGX pod hardware to get those systems and processes integrated into existing factories to avoid getting disrupted by startups. I guess those + AI companies like Google applying LLM derived technologies to robotics is what GP is referring to.
