I've been saying something like this since the 1980s. But we knew back then that manipulation in unstructured situations was very hard. It still is.
Here's the DARPA robot manipulation challenge, 2012.[1] This is pathetic. Especially since DARPA has been funding universities in this area since the 1960s. There's a classic video of robotic assembly at Stanford SAIL in the 1960s I can't find right now. It looks very similar, except that the video quality is worse.
The state of the art in autonomous mobile robots for unstructured environments is terrible. The state of the industry for that is worse. Willow Garage went bust. Google bought up some of the players, ran them into the ground, and dumped them. Schaft, the Tokyo University spinoff they bought, found no buyers at the selloff. (They had nice hardware, too.) Boston Dynamics is still around, feeding off of Softbank now, after feeding off Google and DARPA, but there are no selling products after 30 years. The USMC rejected their Legged Squad Support System. The performance level at the DARPA Humanoid Challenge was very poor.[2]
Even robot vacuum cleaners aren't very good. You'd think they'd be doing offices and stores late at night by now, but they're not. The Roomba, which has the intelligence of an ant (it's from Rod Brooks, the insect AI guy) came out in 2002, and is only slightly smarter 17 years later.
Automatic driving is starting to work, after a few billion dollars was thrown at that problem. That, too, was harder than expected.
Drones, though. Drones are doing fine.
The real breakthrough in machine learning was the discovery that it could be used to target advertising. That doesn't have to work very well to be useful.
It's easy to test. 80% success is fine. Now there's money behind that field.
Embodied AI is really hard to work on, and very expensive. It's easier than it used to be; you can buy decent robot hardware off the shelf, and don't spend your time worrying about gear backlash and motor controllers. But it's still way harder to test than something that runs in a web server.
The payoff is low. Robots in unstructured situations do the jobs of cheap people, and the robots are usually slower. After many decades of many smart people beating their head against the wall in this area, there's been some progress, but not much.
That's why this isn't happening yet.
However, being able to mooch off of technology being developed to serve the ad-supported industries that use AI does help.
I would argue that the issue isn't embodiment, but sensor density.
Humans have an amazing number of sensors built into their manipulators in all directions and an enormous amount of neurological resource dedicated to it.
Until mechanical manipulators have the sensor density of even the back of a finger, it's not really going to get anywhere.
The importance of embodiment has been a fairly common idea in AGI research for many years.
Virtual embodiment has become quite popular. See things like OpenAI gym or DeepMind Lab etc.
Anyway, and this is more of a general comment than a reply to the above comment specifically, this idea is not new, and I hope that people will realize that the field of AGI exists and study some of the existing research. Maybe take a look at the sidebar and intro info at reddit.com/r/agi
> The state of the art in autonomous mobile robots for unstructured environments is terrible.
Roomba’s are very good at what they do. The real limitation is what you want the robot to accomplish and how much it costs. There are surprisingly few home chores worth spending significant amounts on a robot to do it for you vs just having a cheap maid service.
In professional settings, you can generally just make it a structured environment.
I'm not sure you've ever owned a Roomba. In theory they work great. In practice, there's always something on the floor they get tangled in, there's that one couch they're just small enough to fit under but not escape from, or there's that one corner of death in your room they inevitably get into and become trapped. And sometimes, even when everything is absolutely perfect, one of the sensors decides it's stuck an so the thing just backs up in circles indefinitely in an otherwise-ideal empty room.
I've owned two Roombas and both were somehow more work than just sweeping or vacuuming.
We just bought a Mi Robot to replace our Roomba 630. It’s half the price, actually maps my house, doesn’t bump into stuff, has better fault-recovery, scheduling built-in, and is just generally a real pleasure to run.
Provided you want them to clear a surface that's mostly empty of obstructions. Wires are the bane of their existence, but also cloth and big pieces of paper that can't be ingested by the vacuum.
They are relatively ok for office settings, but the models without navigation would take until the universe heat death to clear big open floor offices properly.
Don't the performances of AI in more unstructured game environments show that progress has been mzde? The 5v5 Dota game with OpenAI for example, where the AI is 'embodied' in the héros being played on the terrain.
Here's the DARPA robot manipulation challenge, 2012.[1] This is pathetic. Especially since DARPA has been funding universities in this area since the 1960s. There's a classic video of robotic assembly at Stanford SAIL in the 1960s I can't find right now. It looks very similar, except that the video quality is worse.
The state of the art in autonomous mobile robots for unstructured environments is terrible. The state of the industry for that is worse. Willow Garage went bust. Google bought up some of the players, ran them into the ground, and dumped them. Schaft, the Tokyo University spinoff they bought, found no buyers at the selloff. (They had nice hardware, too.) Boston Dynamics is still around, feeding off of Softbank now, after feeding off Google and DARPA, but there are no selling products after 30 years. The USMC rejected their Legged Squad Support System. The performance level at the DARPA Humanoid Challenge was very poor.[2]
Even robot vacuum cleaners aren't very good. You'd think they'd be doing offices and stores late at night by now, but they're not. The Roomba, which has the intelligence of an ant (it's from Rod Brooks, the insect AI guy) came out in 2002, and is only slightly smarter 17 years later.
Automatic driving is starting to work, after a few billion dollars was thrown at that problem. That, too, was harder than expected.
Drones, though. Drones are doing fine.
The real breakthrough in machine learning was the discovery that it could be used to target advertising. That doesn't have to work very well to be useful. It's easy to test. 80% success is fine. Now there's money behind that field.
Embodied AI is really hard to work on, and very expensive. It's easier than it used to be; you can buy decent robot hardware off the shelf, and don't spend your time worrying about gear backlash and motor controllers. But it's still way harder to test than something that runs in a web server.
The payoff is low. Robots in unstructured situations do the jobs of cheap people, and the robots are usually slower. After many decades of many smart people beating their head against the wall in this area, there's been some progress, but not much. That's why this isn't happening yet.
However, being able to mooch off of technology being developed to serve the ad-supported industries that use AI does help.
[1] https://www.youtube.com/watch?v=jeABMoYJGEU
[2] https://www.youtube.com/watch?v=nIyuC7ceFH0