ABOUT THE SPEAKER
Hod Lipson - Roboticist
Hod Lipson works at the intersection of engineering and biology, studying robots and the way they "behave" and evolve. His work has exciting implications for design and manufacturing -- and serves as a window to understand our own behavior and evolution.

Why you should listen

To say that Hod Lipson and his team at Cornell build robots is not completely accurate: They may simply set out a pile of virtual robot parts, devise some rules for assembly, and see what the parts build themselves into. They've created robots that decide for themselves how they want to walk; robots that develop a sense of what they look like; even robots that can, through trial and error, construct other robots just like themselves.

Working across disciplines -- physics, computer science, math, biology and several flavors of engineer -- the team studies techniques for self-assembly and evolution that have great implications for fields such as micro-manufacturing -- allowing tiny pieces to assemble themselves at scales heretofore impossible -- and extreme custom manufacturing (in other words, 3-D printers for the home).

His lab's Outreach page is a funhouse of tools and instructions, including the amazing Golem@Home -- a self-assembling virtual robot who lives in your screensaver.

More profile about the speaker
Hod Lipson | Speaker | TED.com
TED2007

Hod Lipson: Building "self-aware" robots

Filmed:
1,460,460 views

Hod Lipson demonstrates a few of his cool little robots, which have the ability to learn, understand themselves and even self-replicate.
- Roboticist
Hod Lipson works at the intersection of engineering and biology, studying robots and the way they "behave" and evolve. His work has exciting implications for design and manufacturing -- and serves as a window to understand our own behavior and evolution. Full bio

Double-click the English transcript below to play the video.

00:25
So, where are the robots?
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We've been told for 40 years already that they're coming soon.
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Very soon they'll be doing everything for us.
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They'll be cooking, cleaning, buying things, shopping, building. But they aren't here.
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Meanwhile, we have illegal immigrants doing all the work,
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but we don't have any robots.
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So what can we do about that? What can we say?
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So I want to give a little bit of a different perspective
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of how we can perhaps look at these things in a little bit of a different way.
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And this is an x-ray picture
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of a real beetle, and a Swiss watch, back from '88. You look at that --
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what was true then is certainly true today.
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We can still make the pieces. We can make the right pieces.
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We can make the circuitry of the right computational power,
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but we can't actually put them together to make something
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that will actually work and be as adaptive as these systems.
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So let's try to look at it from a different perspective.
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Let's summon the best designer, the mother of all designers.
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01:27
Let's see what evolution can do for us.
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So we threw in -- we created a primordial soup
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with lots of pieces of robots -- with bars, with motors, with neurons.
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Put them all together, and put all this under kind of natural selection,
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under mutation, and rewarded things for how well they can move forward.
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A very simple task, and it's interesting to see what kind of things came out of that.
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So if you look, you can see a lot of different machines
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come out of this. They all move around.
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They all crawl in different ways, and you can see on the right,
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that we actually made a couple of these things,
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and they work in reality. These are not very fantastic robots,
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but they evolved to do exactly what we reward them for:
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for moving forward. So that was all done in simulation,
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but we can also do that on a real machine.
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Here's a physical robot that we actually
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have a population of brains,
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competing, or evolving on the machine.
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It's like a rodeo show. They all get a ride on the machine,
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and they get rewarded for how fast or how far
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they can make the machine move forward.
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And you can see these robots are not ready
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to take over the world yet, but
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they gradually learn how to move forward,
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and they do this autonomously.
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So in these two examples, we had basically
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machines that learned how to walk in simulation,
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and also machines that learned how to walk in reality.
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But I want to show you a different approach,
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and this is this robot over here, which has four legs.
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It has eight motors, four on the knees and four on the hip.
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It has also two tilt sensors that tell the machine
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which way it's tilting.
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But this machine doesn't know what it looks like.
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You look at it and you see it has four legs,
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the machine doesn't know if it's a snake, if it's a tree,
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it doesn't have any idea what it looks like,
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but it's going to try to find that out.
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Initially, it does some random motion,
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and then it tries to figure out what it might look like.
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And you're seeing a lot of things passing through its minds,
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a lot of self-models that try to explain the relationship
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between actuation and sensing. It then tries to do
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a second action that creates the most disagreement
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among predictions of these alternative models,
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like a scientist in a lab. Then it does that
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and tries to explain that, and prune out its self-models.
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This is the last cycle, and you can see it's pretty much
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figured out what its self looks like. And once it has a self-model,
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it can use that to derive a pattern of locomotion.
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So what you're seeing here are a couple of machines --
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a pattern of locomotion.
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We were hoping that it wass going to have a kind of evil, spidery walk,
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but instead it created this pretty lame way of moving forward.
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But when you look at that, you have to remember
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that this machine did not do any physical trials on how to move forward,
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nor did it have a model of itself.
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It kind of figured out what it looks like, and how to move forward,
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and then actually tried that out.
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(Applause)
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So, we'll move forward to a different idea.
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So that was what happened when we had a couple of --
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that's what happened when you had a couple of -- OK, OK, OK --
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(Laughter)
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-- they don't like each other. So
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there's a different robot.
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That's what happened when the robots actually
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are rewarded for doing something.
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What happens if you don't reward them for anything, you just throw them in?
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So we have these cubes, like the diagram showed here.
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The cube can swivel, or flip on its side,
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and we just throw 1,000 of these cubes into a soup --
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this is in simulation --and don't reward them for anything,
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we just let them flip. We pump energy into this
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and see what happens in a couple of mutations.
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So, initially nothing happens, they're just flipping around there.
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But after a very short while, you can see these blue things
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on the right there begin to take over.
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They begin to self-replicate. So in absence of any reward,
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the intrinsic reward is self-replication.
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And we've actually built a couple of these,
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and this is part of a larger robot made out of these cubes.
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It's an accelerated view, where you can see the robot actually
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carrying out some of its replication process.
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So you're feeding it with more material -- cubes in this case --
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and more energy, and it can make another robot.
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So of course, this is a very crude machine,
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but we're working on a micro-scale version of these,
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and hopefully the cubes will be like a powder that you pour in.
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OK, so what can we learn? These robots are of course
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not very useful in themselves, but they might teach us something
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about how we can build better robots,
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and perhaps how humans, animals, create self-models and learn.
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And one of the things that I think is important
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is that we have to get away from this idea
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of designing the machines manually,
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but actually let them evolve and learn, like children,
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and perhaps that's the way we'll get there. Thank you.
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(Applause)
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ABOUT THE SPEAKER
Hod Lipson - Roboticist
Hod Lipson works at the intersection of engineering and biology, studying robots and the way they "behave" and evolve. His work has exciting implications for design and manufacturing -- and serves as a window to understand our own behavior and evolution.

Why you should listen

To say that Hod Lipson and his team at Cornell build robots is not completely accurate: They may simply set out a pile of virtual robot parts, devise some rules for assembly, and see what the parts build themselves into. They've created robots that decide for themselves how they want to walk; robots that develop a sense of what they look like; even robots that can, through trial and error, construct other robots just like themselves.

Working across disciplines -- physics, computer science, math, biology and several flavors of engineer -- the team studies techniques for self-assembly and evolution that have great implications for fields such as micro-manufacturing -- allowing tiny pieces to assemble themselves at scales heretofore impossible -- and extreme custom manufacturing (in other words, 3-D printers for the home).

His lab's Outreach page is a funhouse of tools and instructions, including the amazing Golem@Home -- a self-assembling virtual robot who lives in your screensaver.

More profile about the speaker
Hod Lipson | Speaker | TED.com