Translator: Martina Pia Ruino Reviewer: Lisa Thompson
Good afternoon, and thank you for inviting me.
I'd like to tell you a simple story about robots.
So, the truly autonomous robot has been a vision, a dream,
of scientists, engineers, and artists for many years.
We're all fascinated by the idea
that one day, it might be possible to construct a thinking machine
that, just as we can, can move and act in our world.
I want you to think, to imagine just for a moment what it might be like
to look into the eyes of a machine and know that it's looking back at you,
that in some even limited sense it has sentience, it has thoughts, intentions,
plans, and maybe one day, even desires.
That's a huge vision. How can we realize it?
We're still a long way from that vision,
but the question I have to answer for you is,
Is it destined to remain science fiction, or is it achievable, in whole or in part?
I believe that it is achievable, and I want to tell you how.
Now, I want to tell you a story in terms of three parts, three waves.
You can think about robotics
and artificial intelligence technology as coming in waves.
Now, that's an approximation to the truth;
it's a mental tool, but I think it's a good first-order approximation.
So, let me tell you about the first wave.
It's what I call structured manipulation,
and in fact, this wave you're all already familiar with.
It's what we call industrial robotics.
So this is a very, very particular technology
that's based on a very, very particular idea,
which is what we call precise control of positions.
So, these industrial robots, they move back and forth
between two or three positions very, very repetitively
and very, very beautifully and accurately.
And this has revolutionized manufacturing.
And so, the problem, however, with these industrial robots,
is that they can't cope with uncertainty or novelty.
So if, for example,
you move the cars in this car factory out of sync by maybe one centimeter,
everything in the factory would go wrong.
And if we want to take those robots out of factories and into our world -
bring them into our world -
we need to be able to deal with that uncertainty and novelty.
So what is it that we need to do that?
Well, we need some kind of intelligence,
and that's where we really come to artificial intelligence.
Now, the thing that you need to understand about AI
is that very early on in AI, we had a lot of big successes.
But they're very interesting
because all the big successes early on in AI were all things like chess,
mathematical theorem proving, medical diagnosis,
and all of those tasks are tasks that when done by people,
we think about as being done by people of high intelligence.
Now, there's a paradox there
because conversely, when you think about simple, everyday tasks
that you don't even think about as requiring thought -
so, for example, walking, seeing, having an everyday conversation,
making a cup of coffee -
all of those things, it turns out,
are far, far harder to automate than we ever anticipated.
Now, in order to be able to do these,
we can't use that kind of old-fashioned AI,
and in fact, although it's been going for a long time,
we've begun to make, over a steady period of time,
progress in a different kind of AI
which is based on machine learning and reasoning under uncertainty,
and this has really been critical
to being able to do these lower level tasks, as it were.
This is a set of images from a task called ImageNet.
This is a competition where different machine learning algorithms
try to learn to recognize things in images.
So, what you do is you have a learning machine -
many of the popular ones are based on principles of the brain -
and you it feed hundreds of thousands of images,
and then it learns to distinguish between, for example, a caravan and a car,
between a motorcycle and a bicycle and a giraffe and so on.
In 2010 to 2014, we suddenly got much better at these kinds of tasks.
There was a breakthrough in machine learning that enabled this.
But hand in hand with machine learning is something we call probabilistic AI,
and it started with, for example, your own James Bernoulli,
but also Thomas Bayes in England - that's the man sitting behind me now -
and they used probability; they came up with the first laws of probability theory,
and this has turned out to be really important for AI.
Bayes, in particular, he came up with this idea
that you could combine your prior beliefs about the world
with evidence from unreliable data, unreliable sensing,
and that's perfect for our robots, but it took us another 300 years
to get the algorithms efficient enough to be able to use them in our robots.
Now, with this machine learning and AI,
we're now able to get the second wave, of unstructured mobility.
So this is things like self-driving cars.
Here, in the picture behind me,
you see a self-driving car with a laser on top.
Now, with that kind of laser-based navigation,
you can localize and build maps in the world,
but the problem is that the laser sitting on top of the car is very expensive;
it generates a lot of data, but it's worth more than the car itself.
So that's not a practical technology.
Instead, it's now the case
that machine learning has advanced to the point
where now we can actually just take a regular camera,
stick it on the top of your dashboard, and it will recognize where it is.
So, this is work done at the University of Oxford,
where the car is able to recognize where it is in the street
based on its previous memories.
And this is a real achievement.
What it's doing is spotting patches between the images that are similar
and are robust to changes in weather or daytime, lighting illumination,
and it learns many specific models like this.
It’s very, very powerful stuff.
However, this kind of revolution in perception
isn't just going to work for cars;
it also works for domestic robots, like this vacuum cleaner,
and what this vacuum cleaner does -
this is work that was done at Imperial College in London,
where it picks out visual landmarks
that it uses to track its way around the house.
So what it's doing is - it never gets lost,
and this means it can plan where it should go to clean,
so it will always go and clean
in the places that are going to be dirtiest or it's cleaned least.
So that's a technology that's really going to revolutionize our lives.
However, there's something missing,
which is these robots have a breakthrough in perception,
but they don't have what we would call cognition;
they can't think ahead in complex ways.
And in fact, that is what we've been looking at.
So, let's imagine that you took your robot out of the box the day that you bought it,
your domestic assistance robot,
and you asked it to get you something for breakfast
and perhaps to get you something to read while you're eating.
But the problem is because you've switched your robot on
and it doesn't know the house, it doesn't know where the kitchen is,
so it can't find the objects that you want.
And in order to address this,
we have to have two very special properties:
first of all, we have to have what we call common sense,
and second, we have to have imagination.
So in my laboratory,
what we've been doing is building robot planning systems
that are capable of reasoning in novel worlds.
So you drop the robot into the office,
and it's able to reason very, very efficiently
about not only how what it does changes the world,
but about how what it does changes what it knows about the world.
And it actually imagines the existence of the things that it has to find
and the kinds of places that it's going to find them,
and it uses this combination of Bayesian reasoning, this probability,
together with artificial intelligence planning techniques
and also common sense.
So it has common sense and imagination.
However, this is still not enough.
This wave of unstructured mobile robots is already revolutionizing our world.
You see it every day when you switch on the TV
and you hear a new story, a new application.
But actually, what we really need
is the ability to bring together the manipulation from the first wave,
together with the ability to deal with unstructured data from the second wave.
And that will give us what we call unstructured manipulation
so that you can go and pick up that cornflakes box and manipulate it.
To see why that's really hard is still beyond us;
it's not hitting us yet and it might not for some time.
This is a robot called Justin, from the German Aerospace Research Institute.
It was a project that I helped work on a little bit.
And Justin is performing a manipulation.
Here he's making a drink, and he has to manipulate these objects,
but it's the case that if you move the objects, it's fine,
but if you changed one of the objects -
so you put a different glass, for example -
Justin would have to be completely reprogrammed.
So, in my laboratory, what we've been doing
is we've been working on planning for machine learning techniques
for grasping novel objects.
So this is Boris; this is one of my robots.
And Boris is able -
you are able to show him how to pick up, for example, a jug,
and to pick it up with a human-type hand,
and then he can see many, many more objects that he's never seen before,
but yet he's able to pick them up.
And he's able, in fact, also,
to imagine lots and lots of possible grasps for these objects,
so hundreds and hundreds of grasps within seconds.
And here we play a few of them for you, and then you can see him executing them.
We'll also be able to show, as you'll be able to see in a moment -
is that he's a very British robot
because of the the way that he picks up his teacup.
(Laughter)
Very polite.
And also, the nice thing here is if you look at the little red dots,
that's all that the robot sensor can see, so not only is the object novel,
but the robot can't actually see very much of the object that it has to manipulate.
However, there's another problem,
which is that here we're using this kind of machine learning technique
in order to be able to deal with positional uncertainty
and shape uncertainty,
but we still, in fact, have the wrong kinds of actuators.
Robots like Boris and Justin
are a little step on from the industrial robots of the past
in the sense that they can feel forces, but only fairly crudely;
they're still basically position control devices.
And we need very, very different kinds of robot hardware
which is much more like our hardware
to be able to manipulate in unstructured environments.
The other thing is we also need new kinds of machine learning
and optimization techniques.
So, for example, here, this is -
techniques people in walking robots
have really begun to crack the problem of how to reason with the robot.
The robot reasons not just about where to put its legs,
but the forces that it needs to apply,
and with this, you can do amazing things.
So this was work done at the University of Southern California,
which involved a colleague of mine who’s now in Birmingham.
So it turns out,
you need the right kind of hardware and also the right kind of brain,
and we still have many problems left to go.
So, in summary, we already have intelligent robots.
Wave one has come; wave two is hitting us now.
That's mobility in unstructured environments.
And wave three will be this unstructured manipulation.
And I want you to imagine how revolutionary that will be
because when we have unstructured manipulating robots,
we will be able to automate any task that humans can do
that involves manipulation,
which is essentially everything except walking.
And is that a good thing?
I believe yes.
If you want to think about the impact of this kind of automation,
go back and imagine what it must have been like
living in the pre-industrial age,
because all of the benefits in our society have all arisen from automation.
So the possibilities at that point really will become almost endless.
Thank you very much.
(Applause)