As an outsider with no knowledge of robotics, I've always been surprised that manipulation tasks (or just smooth robotic movement) are so challenging and seem to progress so slowly, especially when compared to IT.
The amount of feedback built into your end-effectors (pedantic word for human hands) is insane. If you're not familiar, proprioception is a good google/wiki hole. Most of the signals that allow you to move your hands don't even hit the brain stem, let alone the boss upstairs.
The challenge mostly lies in how we've instrumented these things. Precision requires low tolerances. Low tolerances + unexpected environment == you've just driven your robot through the countertop/pan/coworker or broken a very nicely geared servo.
These days we have 3d cameras, but they still only see part of the objects we want to manipulate. The back side is hidden. So you need to either specify and model all objects to interact with, or have some word of a world model where we can predict what the full object, it's weight, center of gravity, surface texture, etc, is like.
And before we even decide to manipulate it, we have to detect it, categorize it and segment it (where does the pan stop and the stove begin?). We have to plan out a manipulation task, including finding grasp points, finding movement patterns that do not interfere with the rest of the environment, etc.
It's a whole bunch of separate problems that need solving all at once. There's motor control, building the right manipulators with the right sensors, bringing all the sensor data into something where we can make a single decision, understanding of the world and what happens during manipulation, and higher level planning.
I'm far removed from this field, and speaking as a layperson, so pardon my ignorance.
We don't even know if "intuition" would arise from the knowledge you claim, we don't know how that model would work, and even before that, collecting all the data (not to speak of availability of all the sensors) is a vastly more complex than even what ChatGPT or any LLM model data collection would ever be.
>it's own experience from reinforcement learning
This is a common mistake often heard from CS -> ML(RL) -> robotics transition folks. Reward function is given for free in RL, but in the real world, estimating the reward is a complex problem in its self. That's why RL on robotics have mostly seen success in quadrupedal locomotion; the reward function is simple (forward velocity, calculated from IMU), but how would you calculate a reward function in 30Hz+ for a simple task such as "chop onion and put it in the pan"? If you can construct the reward function for that task, most likely, you already have all the world-states available and might as well skip RL and do something else with that, such as Model-predictive control.
As for intuition, see: https://en.wikipedia.org/wiki/Moravec%27s_paradox
I love the quote at the end of that article you linked:
> As the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists, and cooks are secure in their jobs for decades to come.
I should've picked a safer career in gardening...
This other content really jumps out at me as well because it's extremely true.
Even older than walking and manual dexterity are really basic abilities like eating. We're nowhere close on that - were so far off it's not on anyone's radar. Robots will run on batteries or some other form of power - there is no way anyone is close to building robots that can eat break down food and use it for energy and repair. One of the oldest evolutionary traits.
The other is course being procreation. Will a robot be able to assemble a new one from pre-made parts? Likely not too far off. But could a robot build or grow one from scratch? That's so far off in the sci-fi future it's silly.
Couldn't we sidestep the complexity of digestion and just get energy from the Sun? With improvements in solar cells and battery technology, we wouldn't need to engineer something as complex as extracting nutrients from food.
I don't think we'd want to replicate biological systems in robots. Digestion and procreation happen at the cellular level, and achieving that with technology is indeed hard sci-fi. Autonomous humanoid robots can exist and be useful for us without this level of sophistication. Though once this happens AI itself will be capable of self-improvement, so we can leave it up to them how they want to improve. I, for one, welcome our new robot overlords. :)
I think one of the issues is that in some parts of academia, progress is made one PhD at a time. And a PhD is almost always too narrow to bring all of these fields together. I'm sure they are solvable problems, and I'm sure they will be solved. But maybe it will take some other research structure? Private? Guaranteed long time funding for academic teams?
So yeah, smooth motion feels easy, but is a gd miracle of biology :)
* Robots can make up for a lack of prediction through really really fast control. This is how Boston Dynamics robots operate at a basic level.
could you elaborate on the mechanical/physical limitations that cause SOTA actuators to lag behind muscles, and if there's an equivalent "moore's law" that might predict when this gap closes appreciably, if ever?
No doubt there is lots of much more advanced reasons. These are just from the top of my head.
Basically, such a motor can lift two chocolate bars (2x100g) at arms length.