How robots are grasping the art of gripping
nature.com
nature.com
IE: Stand up on two legs, walk forward, and move these 10 objects from this bin to that bin without breaking any of those objects. Or fold these clothes (surprisingly difficult to get a robot to do that).
We can build super-advanced Chess and Go AIs that beat out the grand summation of theoretical human knowledge (with Chess theory going back hundreds of years, and Go theory going back maybe thousands!!), but we still can't move a variety of objects from one bin to another successfully and repeatably.
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Another funny example: AIs (or various algorithms at least) have solved the object camera tracking problem, but still basically fail at depth perception and figuring out if something is "still" or "moving" based on sight alone.
The things we think are easy, we've been trained to do through millions of years of evolution. Running with two feet through a forest is pretty easy for us. Not so much so for computers.
The things that we think are mentally taxing, we've only been doing for a few thousand years or so.
Easy for a twelve year old who has practicing motor skills for their entire life. And who would still be spastic on some assembly lines
It's kind of fascinatingly serendipitous in the sense that computers/AI are super quick at performing the tasks that we humans find extremely taxing (complex mathematical computation, visual simulation) while struggling with tasks that comes naturally to us humans (gripping objects, running with two feet--as you described) owing to evolution.
I wonder how much of that is because of constituent 'building block' materials that make up the basic structure of homo sapiens and computers -- carbon and silicon, respectively.
An interesting counter-example to the trend is theorem proving. A symbolic task that wasn't present in the ancestral environment, but humans crush computers at it.
Perhaps the lesson to learn is that we are not as good as we think at board games and maths, etc.
This is the baseless myth that won't die. We are nowhere near rivaling humans in mathematical proving. Even after a human has proved a theorem, it can be an astronomic amount of work to even explain the proof to a machine after the fact (i.e. to construct a machine-checkable version of the proof), even when using suites of sophisticated software tools developed solely to facilitate this process. Progress has been glacially slow since the field began in the 1950s. Deep learning has had zero impact.
We will have robots that can grasp competently way before we have machines that can rival humans in mathematical proving.
But move down to boolean logic and symbolic computation... the stuff that translates statements in Verilog or VHDL into pure logic statements for small LUTs in FPGAs or logical NAND gates of hardware synthesis??
Oh yeah, machines are way better than humans. People pretty much rely upon automatic synthesis for circuit design and boolean logic these days. Proof is in the pudding.
Perhaps it isn't the same kind of math you were thinking about. But Boolean algebra is still math, logic, and proofs. Of course, in practice, humans use these tools to build larger structures... but I'd argue that the machine handles a lot of the rote proof stuff (optimally figuring out the minimum number of NAND gates to represent a certain truth table).
The state of the art is still bad, and it's been bad for a long time. Watch these two videos: 1960s: [1] 2012: [2] Note how little progress there has been.
Here's the actual winner of the 2018 Amazon picking challenge.[3] The system mentioned in the article did not win.
Throwing deep learning at the problem helps, but not all that much. It's amazing how hard this is.
[1] https://archive.org/details/sailfilm_pump [2] https://www.youtube.com/watch?v=jeABMoYJGEU [3] https://www.youtube.com/watch?v=AljePt7Mh6U
Even if it's not a total flop, it can degrade the brand. Holiday Inn Express and Hilton's low-end product lines are examples. The overall result was to move the brand downscale.
[1] https://www.adweek.com/brand-marketing/best-and-worst-brand-...
And second, there's some decent progress i think:
Picking chocolates without harming them:
https://www.youtube.com/watch?v=EcuT0RWRpow
The same company also does an example of unstructured bin picking:
https://www.youtube.com/watch?v=UZAQ351yViM
Altough it's hard to tell if it can do sorting(or object understanding" from that video.
[1] https://www.youtube.com/watch?v=m7l-87r4oOY [2] https://www.youtube.com/watch?v=u4ZScJsaepg
Animals basically have a feedback loop that works a little bit with proprioception / position measures and way more with expectations of tactile sense (to see this, reflect on how you move in the dark: not thinking about your joint angles, but about that not to kick!).
As a hobbyist, for example, there is no way I can have a "video" of pressure maps on an artificial skin, so I can only hack my way with a one-dimensional finger-mounted pressure sensor. How on earth can I program a robot to use his hand only by looking at it? I would magine that most researchers are in a similar way restricted to work with subpar feedback loops.
Moving a cat body, at 130 kph, on an angle that will take the front claw (they always use the same one) across the throat of a wildly jumping around animal (with enough force to allow the claw to rip it open) while keeping that same cat body away from any hooves (one hit from even deer hooves will at the very least end the hunt of any puma, and can do damage up to killing it) is ... it's not just ridiculously hard. It's almost absurd what sort of control you'd need.
I'm also not very sure how tactile sensors help, even if they could not just register contacts but air speeds as well, that does not seem like it would make the problem much easier.
Plus cats are a very "general" body. The number of ways it can move, because it lacks a fully connected inner bone structure (so almost any cat joint can move remarkably far in almost any direction). I'm pretty sure this adds insult to injury and makes it a lot harder yet again.
Now we hear there's progress in gripping.
The thing about both these steps is walking and grasping are extremely challenging tasks to robots but even more challenging is the easy integration of these and other movements that humans achieve when doing "simple tasks" and especially cooperating in simple tasks - in the "unstructured environments" referenced in the article.
Better gripping, though, has immediate practical applications in manufacturing and distribution. We're much more likely to see that in use in the near future.
A human walking uses about 80 Watts [1]. Assuming no losses that requires a 1/3 m2 solar panel (a perfect one, constantly in full sunlight) to just move during the day. So count on needing 2/3 m2 at the very least.
And while robotics have advanced, there is no robot I know of that uses less than 300 or so watts.
I think you're right though that there is a market for very large, very heavy vehicles with legs, for where slopes are either too steep, or the region just doesn't have roads. Such vehicles exist, but humans just can't control 4 legged vehicles. It quickly ends in disaster.
[1] https://en.wikipedia.org/wiki/Human_power
[2] https://www.google.com/search?q=solar+power+output+per+squar...
Software is the main bottleneck in ML at the moment, imo. That and data. The hardware problem (computation), which the author discusses briefly, has been more or less solved in the industry as newer software require less miniaturization of ICs and peripheral components[0] -- this despite Moore's law effect wearing off. [1]
It's all about striking a fine balance between the hardware's raw computational capability and designing compatible software nimble enough to adapt rapidly to colossal influx of data.
[0] https://www.forbes.com/sites/quora/2017/01/27/has-moores-law... [1] https://www.technologyreview.com/s/601441/moores-law-is-dead...
In the field of robotics I'd say the hardware still has a lot of room for improvement, although I'd still say software is the bigger bottleneck.
I stand corrected. Thank you for pointing it out.
I'm only a casual long time observer, but I think there's an opportunity here for haptic teleoperation. Data could be gathered from haptic teleoperators in much the same way that Tesla is gathering data for self-driving AI. Back in the 90's and early 2000's, Sarcos corp's website used to have a crappy realmedia video of a full-arm haptic rig operating an over-sized hydraulic arm casually holding an anvil like a beer mug. So evidently, we've been able to do full-body haptics and human-level agility manipulators for quite awhile. (Research in that stuff actually started in the 1970's!) On-orbit teleoperation from the ground could have interesting applications.
Note, however, that most robots today actually operate in spaces designed for machines - ie, factories. Getting robots to operate in the "unstructured spaces" the article mentions is extremely difficult and hasn't been cost effective.
A large amount of gains in productivity have been achieved by substituting machines for humans, with "robots" just being the most programmable of machines. But very few of those gains involve direct substitution of machine movement for human movement - rather it's involved imposing structure on the whole productive environment so the rigid motion of a machine has a predictable result.
I feel like that's starting to shift a bit where folks are realizing that more automation by robotics isn't actually reducing the potential workforce, it's just shifting it. Again though, it's up to the employers and manufacturing companies to really drive that point home.
It's an exciting time. :)
That doesn't really reflect the situation. Machines have been substituted for humans since at least Eli Whitney's Cotton Gin. Substituting machines for humans has had nothing to do with providing safety, rather it has been driven by the desire for increased productivity and thereby profits. Certainly, this increased has provided massive benefit to society along with various drawbacks. That's literally the history of the "industrial revolution".
Well, OK, except for the long history of industrial automation that doesn't bear this out at all. There has resistance to automation from the English Luddites to Detroit Black Workers Union and beyond and arguments that automation is only OK safety is involved have not entered the picture that entire period.
Basically, there's never been a situation where public complaints stopped automation. It has proceeded as fast as technology allowed to the present.
EP Thompson is the reference for early automation and the luddites but it's hard to give a reference for a negative.
If you have any reference for situation where public sentiment limited automation, I'd love to see them. Because it sounds like you are mistakenly thinking the world actually works according to the vague headlines one see sees periodically.
Already found: "Universal robotic gripper based on the jamming of granular material" http://www.pnas.org/content/107/44/18809
> Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight.
Training a robot to fold laundry would be like training an astronaut to fold laundry in Apollo era gloves (which btw, I have read were so bad, the lunar walkers lost fingernails inside 'em)
If I may just interject, totally calmly like, the world was not designed, and certainly not for our hands.