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.

People think robots in terms of humanoid or number-5 style robots.

I think it'll be more capable appliances at first.

Like a lawn mowing device that also spots weeds and can spray them.

Next iteration has arms to rip weeds out of the garden.

Next has attachments so you can direct it to do pruning.

Next it can figure out the pruning itself and move the outcome into the woodchipper.

And so on and so on.

It's not going to be one day a humanoid robot comes into the house and does everything.

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.

This is why im not worried about "AI" taking over the world. Robotics still has a LONG way to go. A human can balance a plate on their arm with food while holding a glass of milk in that hand and a donut in the other and still manage to open a door, step over potential floor obstacles, maneuver tight spaces, get bumped by a child or dog, and still set it all down without spilling it 99% of the time. Just the hardware with the dexterity and responsiveness to perform the same task would cost unimaginable amounts of money to produce, not to mention the control systems needed to do it smooth and gracefully enough.

Maybe in another 2 decades I could see it possibly starting to change, but even then I wouldn't bet the horse on it until I saw it. Cars only have three degrees of freedom and even that we are barely able to get working well enough to put it into limited practice. And yet one single human finger has atleast 3 degrees of freedom, and is covered in what is the equivalent of a million tiny ultra sensitive tactile sensors.

“If a self-driving car finds itself in a situation it can’t handle or suffers a glitch, it can pull over or, in the worst case, just hit the brakes.”

Wrong. Try hitting the brakes of your self driving car on highway at 65mph or during unprotected left turn with oncoming vehicles.

Or have a glitched self-driving car hit its brakes and block the road, for emergency vehicles, and endangering other people.

Self-driving cars can also suffer a glitch without knowing they suffered a glitch, like Waymo cars driving into flooded roads.

“I may not care if my household robot takes all night to tidy up and fold the laundry.”

I do. I don’t want robot vacuuming or making noise at night or doing something potentially dangerous unmonitored while people are asleep.

> I am confused at how Waymo engineering can be so robust as to yield an astonishingly good safety record, and yet so slapdash as to happily drive into deep water.

I feel this is actually somewhat straightforward. I assume deep water on roadways is not commonly in the training set, because frankly it isn't common in real life, and when it is common people do not drive and do not gather that training data. As a result the proper response has not adequately been beaten into the models. There are probably also challenges of world-sensing, since water can act as a mirror, and maybe other complications. So waymos are bad at handling deep water on roadways. However, deep water on roadways is also not common in the areas where waymos are deployed. As a result, waymo's have a great safety record, and at the same time they make mistakes that are obvious to a human.

A common criticism of AI discourse is that people act as if LLM's "think". I don't want to be a vocabulary purist, but I suspect that's related to the astonishment here -- the Waymo doesn't know what flooding is, it doesn't fear drowning, it doesn't think. So unless it's been repeatedly trained, or a special case has been hard coded by manual effort, it doesn't know that flooded roadways are dangerous.

I have made a lot of assumptions here, and I don't truthfully know what the training data looks like. Feel free to push back if you think my assumptions are wrong. I'd especially be interested if somebody can show that water on roadways _is_ in the training data

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...

I mean, functional robotics isn't that hard, robotic vacuums have been in homes for a decade now, and industrial robotic arms. However, humanoid robots IS hard mainly because of the form factor constraints. You can't really get a lot of power out of servo motors and other actuator if it all has to be self-contained in a humanoid form instead of using hydraulics or pneumonic or even big stepper motors as for stationary industrial robots.

The videos I've seen of humanoid robot applications are basically that it can do dishes and fold laundry, but I think if household chore robots ever come to market, they would probably not look humanoid at all and probably look like semi dishwashers/washing machines with wheels and a gripper arm.

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!

Another example of how AI dumbs down everything.

> Once they have context, robots will need to reason, plan, and exercise judgement and common sense. LLM-based systems like ChatGPT and Claude are making great strides in these areas

But why would I want to make AI more powerful - and disruptive - than it already is? I don't see this as a benefit but as a disadvantage. Let's also not forget that e. g. Google deliberately ruined its search engine. Now if you search something, by default, you get AI slop results that are often not truthful or only partially truthful. This is a private web. Google wants to control information.

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.