Reasons robotics is hard

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&

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.

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.

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.

A robotic warehouse that's just like a human warehouse but with robots walking the aisles will always be more expensive (and likely far less efficient) than a warehouse purpose built for automated picking using standard containers and graspers, conveyer belts or path constrained wheeled platforms--something I've seen in operation 20 years ago.

A factory of general purpose robots sewing t-shirts will always be more expensive than a factory a low wage humans sitting there doing the same.

The Fourdrinier process for making flat-sheet goods (i.e., paper, thin plastic, in massive rolls) is more than two centuries old. The idea that robots, returning to dipping a mould into the furnish to create individual sheets, could even come close to the economics of modern papermaking is insane.

[0] https://en.wikipedia.org/wiki/Paper_machine

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.

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

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

The most visible "progress" of so-called AI is in activities that take place at the interface between computers and gullible humans.

The reason so much less progress has been made in robots is that real-world physics isn't gullible.

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

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

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.

> 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.

https://youtube.com/watch?v=weXDUc5Osto

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