Reasons robotics is hard (secondthoughts.ai)

A marker of progress will be when Amazon converts to automated picking. They've been trying hard for almost a decade now. They had an annual competition for years. They have a decent picking robot developed in house.[1] It's not being deployed in quantity yet. Nor does it have anything like a humanoid hand. Just a two-surface gripper. Amazon's production robots are mostly automatic guided vehicles, not manipulators.

I'm impressed with how far legged locomotion has come. But as yet, nobody seems to be using legged robots for any commercial purpose beyond the demo level.

Is Tesla still going to produce vast numbers of humanoid robots by the end of 2026?

There's been a lot of progress on the hardware side. Motor technology from drones has produced much better robot motors. The sweet spot on gear reduction seems to have been found. (Too much reduction, and you can't back drive. Too little, and the motors have to be too big.) The volumes are now large enough to justify making robot-specific components. Robot arms are much better than a decade ago. So are robot legs. Control is better, too. It looks like a humanoid robot will cost about as much as a car.

But they're still not quite good enough to be useful.

We'll know they are real when an Amazon Prime truck drives up and a robot does the last 100 meters of the delivery.

[1] https://www.youtube.com/watch?v=WStK9HNn8c8&

Roboticist here. All of this, and he didn’t mention compliance or online adaptation to otherwise un-sensable dynamics. Or massively complex miniature mechanisms.

Current generation tactile sensors cost a couple thousand $ PER FINGER, and have a real world MTBF of hours. The cost can be solved with economy of scale. The fragility is harder.

Yes the problem is very hard. Mainly because high DOF generalization is very difficult.

We have self driving cars because what are the control inputs? Pedal, brake, steering wheel. This already took many many years.

Now for a humanoid robot: An action space that is metaphorically Hilbert. (Physically, yes, obviously)

Also, IMO, LLM's can aid the development of robots, but do little beyond a planning, human control interface. Below that it's the domain of control and the solution will be the correct combination of classical, neural, and real time optimization based control.

All the bad-ass biped robots that actually look natural? It's PID controls wrapped with control barrier functions constraining the QPs that are being solved in real time.

But that's annoying to derive per-application. So we'll need neural methods which can be learned (while being constrained by a priori knowledge of dynamics). My hunch is that the Yann LeCunn type of jepa models will be how tasks can be learned.

Couple of related reads/watches to this I found useful recently, with a similar conclusion:

https://www.youtube.com/watch?v=FUUzmRH5Yi4

https://www.construction-physics.com/p/robot-dexterity-still...

people very soon as going to look back at all of us and just think "they didn't even have robots yet! how did they even eat?" Well, future readers, it isn't easy! We either go to the store and bring back groceries ourselves or pay a helper to do it. No robot in the loop there. Then when it comes time to cook something we either have to cut it up and otherwise prepare it and then cook it ourselves, or another human can do it, or we can buy fully prepared foods that don't require cooking. And then when we're done we have to put our own dishes away into a dishwasher. We can't just say a few words and have a robot do it for us!

hairstylists are safe. and manicurists. and outcall masseurs.

really anything that involves a bade near your body or where body contact is the point.

The physical world is basically an infinite amount of global state that must be perceived indirectly through imperfect sensors and acted on using imperfect motors and manipulators.

I can say many things, but most people here will not believe me. I'd say just watch China do the 'impossible'. Then some self reflection should be in order.